CREDIT-BASED INSURANCE SCORES: 

IMPACTS ON CONSUMERS 

OF AUTOMOBILE INSURANCE 

A Report to Congress by the 

Federal Trade Commission 

July 2007 
 

 
FEDERAL TRADE COMMISSION 

Deborah Platt Majoras Chairman 

Pamela Jones Harbour Commissioner 

Jon Leibowitz Commissioner 

William E. Kovacic Commissioner 

J. Thomas Rosch Commissioner 

Bureau of Economics 

Michael R. Baye Director 

Paul A. Pautler Deputy Director for Consumer Protection 

Jesse B. Leary Assistant Director, Division of Consumer Protection 

Bureau of Consumer Protection

Lydia B. Parnes Director 

Mary Beth Richards Deputy Director 

Peggy Twohig Associate Director, Division of Financial Practices 

Thomas B. Pahl Assistant Director, Division of Financial Practices 

Analysis Team 

Matias Barenstein, Economist, Bureau of Economics, Div. of Consumer Protection 

Archan Ruparel, Research Analyst, Bureau of Economics, Div. of Consumer Protection 

Raymond K. Thompson, Research Analyst, Bureau of Economics, Div. of Consumer Protection 

Other Contributors 

Erik W. Durbin, Dept. Assistant Director, Bureau of Economics, Div. of Consumer Protection 

Christopher R. Kelley, Research Analyst, Bureau of Economics, Div. of Consumer Protection 

Kenneth H. Kelly, Economist, Bureau of Economics, Div. of Consumer Protection

Michael J. Pickford, Research Analyst, Bureau of Economics, Div. of Consumer Protection 

W. Russell Porter, Economist, Bureau of Economics, Div. of Consumer Protection

i

TABLE OF CONTENTS i 

LIST OF TABLES iii 

LIST OF FIGURES iv 

I. EXECUTIVE SUMMARY 1 

II. INTRODUCTION 5 

III. DEVELOPMENT AND USE OF CREDIT-BASED INSURANCE SCORES 7 

A. Background and Historical Experience 7 

B. Development of Credit-Based Insurance Scores 12 

C. Use of Credit-Based Insurance Scores 15 

D. State Restrictions on Scores 17

IV. THE RELATIONSHIP BETWEEN CREDIT HISTORY AND RISK 20

A. Correlation Between Credit History and Risk 20 

1. Prior Research 20 

2. Commission Research 23 

a. FTC Database 23 

b. Other Data Sources 28 

B. Potential Causal Link between Scores and Risk 30 

 

V. EFFECT OF CREDIT-BASED INSURANCE SCORES ON PRICE 

 AND AVAILABILITY 34 

A. Credit-Based Insurance Scores and Cross-Subsidization 35 

1. Possible Impact on Car Ownership 39 

2. Possible Impact on Uninsured Driving 40 

3. Adverse Selection 42 

B. Other Possible Effects of Credit-Based Insurance Scores 46 

C. Effects on Residual Markets for Automobile Insurance 49 

VI. EFFECTS OF SCORES ON PROTECTED CLASSES OF CONSUMERS 50 

A. Credit- Based Insurance Scores and Racial, Ethnic, and Income Groups 51 

1. Difference in Scores Across Groups 51 

2. Possible Reasons for Differences in Scores Across Groups 56 

3. Impact of Differences in Scores on Premiums Paid 58 

a. Effect on Those for Whom Scores Were Available 58 

b. Effect on Those for Whom Scores Were Not Available 59 

B. Scores as a Proxy for Race and Ethnicity 61 

1. Do Scores Act Solely as a Proxy for Race, Ethnicity, or Income? 62 

2. Differences in Average Risk by Race, Ethnicity, and Income 64 

3. Controlling for Race, Ethnicity, and Income to Test for a Proxy Effect 67 

a. Existence of a Proxy Effect 67 

b. Magnitude of a Proxy Effect 69 
ii

VII. ALTERNATE SCORING MODELS 73 

A. The FTC Baseline Model 74 

B. Alternative Scoring Models 78 

1. “Race Neutral” Scoring Models 78 

2. Model Discounting Variables with Large Differences by Race and 

Ethnicity 80 

VIII. CONCLUSION 82 

TABLES 

FIGURES 

APPENDIX A. Text of Section 215 of the FACT ACT 

APPENDIX B. Requests for Public Comment 

APPENDIX C. The Automobile Policy Database 

APPENDIX D. Modeling and Analysis Details 

APPENDIX E. The Score Building Procedure 

APPENDIX F. Robustness Checks and Limitations of the Analysis 
iii

TABLES 

TABLE 1. Typical Information Used in Credit-Based Insurance Scoring Models 

TABLE 2. Claim Frequency, Claim Severity, and Average Total Amount Paid on 

Claims 

TABLE 3. Median Income and Age, and Gender Make-Up, by Race and Ethnicity 

TABLE 4. Change in Predicted Amount Paid on Claims from Using Credit-Based 

Insurance Scores, by Race and Ethnicity 

TABLE 5. Estimated Relative Amount Paid on Claims, by Race, Ethnicity, and 

Neighborhood Income 

TABLE 6. Estimated Relative Amount Paid on Claims, by Score Decile, Race, 

Ethnicity, and Neighborhood Income 

TABLE 7. Change in Predicted Amount Paid on Claims from Using Credit-Based 

Insurance Scores Without and With Controls for Race, Ethnicity, and 

Income, by Race and Ethnicity 

TABLE 8. Change in Predicted Amount Paid on Claims from Using Other Risk 

Variables, Without and With Controls for Race, Ethnicity, and Income, by 

Race and Ethnicity 

TABLE 9. Baseline Credit-Based Insurance Scoring Model Developed by the FTC 

TABLE 10. Credit-Based Insurance Scoring Model Developed by the FTC by 

Including Controls for Race, Ethnicity, and Neighborhood Income in the 

Score-Building Process 

TABLE 11. Credit-Based Insurance Scoring Model Developed by the FTC Using a 

Sample of Only Non-Hispanic White Insurance Customers 

TABLE 12. Credit-Based Insurance Scoring Model Developed by the FTC by 

Discounting Variables with Large Differences Across Racial and Ethnic 

Groups 

iv

FIGURES 

FIGURE 1. Estimated Average Amount Paid Out on Claims, Relative to Highest 

Score Decile 

FIGURE 2. Frequency and Average Size (Severity) of Claims, Relative to Highest 

Score Decile 

FIGURE 3. "CLUE" Claims Data: Average Amount Paid Out on Claims, Relative to 

Highest Score Decile 

FIGURE 4. By Model Year of Car: Estimated Average Amount Paid Out on Claims, 

Relative to Highest Score Decile (Property Damage Liability Coverage) 

FIGURE 5. Change in Predicted Amount Paid on Claims from Using Scores 

FIGURE 6. The Ratio of Uninsured Motorist Claims to Liability Coverage Claims 

(1996-2003) 

FIGURE 7. Share of Cars Insured through States' "Residual Market" Insurance 

Programs (1996-2003) 

FIGURE 8. Distribution of Scores, by Race and Ethnicity 

FIGURE 9. Distribution of Race and Ethnicity, by Score Decile 

FIGURE 10. Distribution of Scores, by Neighborhood Income 

FIGURE 11. Distribution of Neighborhood Income, by Score Decile 

FIGURE 12. Distribution of Scores by Race and Ethnicity, After Controlling for Age, 

Gender, and Neighborhood Income 

FIGURE 13. By Race and Ethnicity: Change in Predicted Amount Paid on Claims from 

Using Scores, by Race and Ethnicity 

FIGURE 14. By Race and Ethnicity: Estimated Average Amount Paid Out on Claims, 

Relative to Non-Hispanic Whites in Highest Score Decile 

FIGURE 15. By Neighborhood Income: Estimated Average Amount Paid Out on 

Claims, Relative to People in Highest Score Decile in High Income Areas 

FIGURE 16. Estimated Average Amount Paid Out on Claims, Relative to Highest 

Score Decile, with and without Controls for Race, Ethnicity, and 

Neighborhood Income
v

FIGURE 17. FTC Baseline Model - Estimated Average Amount Paid Out on Claims, 

Relative to Highest Score Decile 

FIGURE 18. Distribution of FTC Baseline Model Credit-Based Insurance Scores, by 

Race and Ethnicity 

FIGURE 19. FTC Score Models with Controls for Race, Ethnicity, and Neighborhood 

Income: Estimated Average Amount Paid Out on Claims, Relative to 

Highest Score Decile 

FIGURE 20. Distribution of FTC Credit-Based Insurance Scores, by Race and Ethnicity 

FIGURE 21. An Additional FTC Credit-Based Insurance Scoring Model: The 

"Discounted Predictiveness" Model Estimated Average Amount Paid Out 

on Claims, Relative to Highest Score Decile 

FIGURE 22. Distribution of FTC Credit-Based Insurance Scores, by Race and Ethnicity 
1

I. EXECUTIVE SUMMARY

Section 215 of the FACT Act (FACTA)1 requires the Federal Trade Commission 

(FTC or the Commission) and the Federal Reserve Board (FRB), in consultation with the 

Department of Housing and Urban Development, to study whether credit scores and 

credit-based insurance scores affect the availability and affordability of consumer credit, 

as well as automobile and homeowners insurance. FACTA also directs the agencies to 

assess and report on how these scores are calculated and used; their effects on consumers, 

specifically their impact on certain groups of consumers, such as low-income consumers, 

racial and ethnic minority consumers, etc.; and whether alternative scoring models could 

be developed that would predict risk in a manner comparable to current models but have 

smaller differences in scores between different groups of consumers. The Commission 

issues this report to address credit-based insurance scores2

 primarily in the context of 

automobile insurance.3

Credit-based insurance scores, like credit scores, are numerical summaries of 

consumers’ credit histories. Credit-based insurance scores typically are calculated using 

information about past delinquencies or information on the public record (e.g., 

bankruptcies); debt ratios (i.e., how close a consumer is to his or her credit limit); 

evidence of seeking new credit (e.g., inquiries and new accounts); the length and age of 

credit history; and the use of certain types of credit (e.g., automobile loans). Insurance 

 

1

 15 U.S.C. § 1681 note (2006). Appendix A contains the complete text of Section 215 of the FACT Act. 

2

 The FRB will submit a report addressing issues related to the use of credit scores and consumer credit 

decisions. 

3

 The Commission will conduct an empirical analysis of the effects of credit-based insurance scores on 

issues relating to homeowners insurance; the FTC anticipates that it will submit a report to Congress 

describing the results of this analysis in early 2008. 
2

companies do not use credit-based insurance scores to predict payment behavior, such as 

whether premiums will be paid. Rather, they use scores as a factor when estimating the 

number or total cost of insurance claims that prospective customers (or customers 

renewing their policies) are likely to file. 

Credit-based insurance scores evolved from traditional credit scores, and 

insurance companies began to use insurance scores in the mid-1990s. Since that time, 

their use has grown very rapidly. Today, all major automobile insurance companies use 

credit-based insurance scores in some capacity. Insurers use these scores to assign 

consumers to risk pools and to determine the premiums that they pay. 

 Insurance companies argue that credit-based insurance scores assist them in 

evaluating insurance risk more accurately, thereby helping them charge individual 

consumers premiums that conform more closely to the insurance risk they actually pose. 

Others criticize credit-based insurance scores on the grounds that there is no persuasive 

reason that a consumer’s credit history should help predict insurance risk. Moreover, 

others contend that the use of these scores results in low-income consumers and members 

of minority groups paying higher premiums than other consumers. 

 Pursuant to FACTA, the FTC evaluated: (1) how credit-based insurance scores are 

developed and used; and, in the context of automobile insurance (2) the relationship 

between scores and risk; (3) possible causes of this relationship; (4) the effect of scores 

on the price and availability of insurance; (5) the impact of scores on racial and ethnic 

minority groups and on low-income groups; and (6) whether alternative scoring models 

are available that predict risk as well as current models and narrow the differences in 

scores among racial, ethnic, and other particular groups of consumers. In conducting this 

evaluation, the Commission considered prior research, nearly 200 comments submitted in 
3

response to requests for the public’s views, information presented in meetings with a 

variety of interested parties, and its own original empirical research using a database of 

automobile insurance policies. Based on a careful and comprehensive consideration of 

this information, the FTC has reached the following findings and conclusions: 

● Insurance companies increasingly are using credit-based insurance scores 

in deciding whether and at what price to offer coverage to consumers. 

● Credit-based insurance scores are effective predictors of risk under 

automobile policies. They are predictive of the number of claims 

consumers file and the total cost of those claims. The use of scores is 

therefore likely to make the price of insurance better match the risk of loss 

posed by the consumer. Thus, on average, higher-risk consumers will pay 

higher premiums and lower-risk consumers will pay lower premiums. 

● Several alternative explanations for the source of the correlation between 

credit-based insurance scores and risk have been suggested. At this time, 

there is not sufficient evidence to judge which of these explanations, if 

any, is correct. 

 

● Use of credit-based insurance scores may result in benefits for consumers. 

For example, scores permit insurance companies to evaluate risk with 

greater accuracy, which may make them more willing to offer insurance to 

higher-risk consumers for whom they would otherwise not be able to 

determine an appropriate premium. Scores also may make the process of 

granting and pricing insurance quicker and cheaper, cost savings that may 

be passed on to consumers in the form of lower premiums. However, little 

hard data was submitted or available to quantify the magnitude of these 

benefits to consumers. 

● Credit-based insurance scores are distributed differently among racial and 

ethnic groups, and this difference is likely to have an effect on the 

insurance premiums that these groups pay, on average. 

▪ Non-Hispanic whites and Asians are distributed relatively evenly 

over the range of scores, while African Americans and Hispanics 

are substantially overrepresented among consumers with the 

lowest scores (the scores associated with the highest predicted risk) 

and substantially underrepresented among those with the highest 

scores. 

▪ With the use of scores for consumers whose information was 

included in the FTC’s database, the average predicted risk (as 

measured by the total cost of claims filed) for African Americans 
4

and Hispanics increased by 10% and 4.2%, respectively, while the 

average predicted risk for non-Hispanic whites and Asians 

decreased by 1.6% and 4.9%, respectively. 

● Credit-based insurance scores appear to have little effect as a “proxy” for 

membership in racial and ethnic groups in decisions related to insurance. 

▪ The relationship between scores and claims risk remains strong 

when controls for race, ethnicity, and neighborhood income are 

included in statistical models of risk. 

▪ In models with credit-based insurance scores but without controls 

for race or ethnicity, African Americans and Hispanics are 

predicted to have average predicted risk 10% and 4.2% higher, 

respectively, than if scores were not used. In models with scores 

and with controls for race, ethnicity, and income, these groups 

have average predicted risk 8.9% and 3.5% higher, respectively 

than if scores were not used. The difference between these two 

predictions for African Americans and Hispanics (1.1% and 0.7%, 

respectively) is a measure of the effect of scores on these groups 

that is attributable to scores serving as a statistical proxy for race 

and ethnicity. 

▪ Several other variables in the FTC’s database (e.g., the time period 

that a consumer has been a customer of a particular firm) have a 

proportional proxy effect that is similar in magnitude to the small 

proxy effect associated with credit-based insurance scores. 

▪ Tests also showed that scores predict insurance risk within racial 

and ethnic minority groups (e.g., Hispanics with lower scores have 

higher estimated risk than Hispanics with higher scores). This 

within-group effect of scores is inconsistent with the theory that 

scores are solely a proxy for race and ethnicity. 

 

● After trying a variety of approaches, the FTC was not able to develop an 

alternative credit-based insurance scoring model that would continue to 

predict risk effectively, yet decrease the differences in scores on average 

among racial and ethnic groups. This does not mean that a model could 

not be constructed that meets both of these objectives. It does strongly 

suggest, however, that there is no readily available scoring model that 

would do so. 
5

II. INTRODUCTION

Over the past decade, insurance companies increasingly have used information 

about credit history in the form of credit-based insurance scores to make decisions 

whether to offer insurance to consumers, and, if so, at what price. Because of the 

importance of insurance in the daily lives of consumers, the widespread use of these 

scores raises questions about their impact on consumers. In particular, some have 

expressed concerns about the effect of scores on the availability and affordability of 

insurance to members of certain demographic groups, especially racial and ethnic 

minorities. 

In 2003, Congress enacted the Fair and Accurate Credit Transactions Act 

(FACTA) to make comprehensive changes to the nation’s system of handling consumer 

credit information. In response to concerns that had been raised about credit-based 

insurance scores, in Section 215 of FACTA Congress directed certain federal agencies, 

including the FTC, to conduct a broad and rigorous inquiry into the effects of these scores 

and submit a report to Congress with findings and conclusions. The report is intended to 

provide policymakers with critical information to enable them to make informed 

decisions with regard to credit-based insurance scores. 

Section 215 of FACTA sets forth specific requirements for studying the effects of 

credit-based insurance scores in the context of automobile and homeowners insurance. It 

directs the agencies to include a description of how these scores are created and used, as 

well as an assessment of the impact of scores on the availability and affordability of 

automobile and homeowners insurance products. Section 215 also requires a rigorous 

and empirically sound statistical analysis of the relationship between scores and 

membership in racial, ethnic, and other protected classes. The mandated study further 
6

must evaluate whether scores act as a proxy for membership in racial, ethnic, and other 

protected classes. Finally, Section 215 requires an analysis of whether scoring models 

could be constructed that both are effective predictors of risk and result in narrower 

differences in scores among racial, ethnic, and other protected classes. 

Section 215 of FACTA also specifies the process to be used in conducting the 

study, and the contents of the report to be submitted. The Act directed the agencies to 

seek input from federal and state regulators and consumer and civil rights organizations, 

and members of the public concerning methodology and research design. The Act 

requires the report to include “findings and conclusions of the Commission, 

recommendations to address specific areas of concerns addressed in the study, and 

recommendations for legislative or administrative action that the Commission may 

determine to be necessary to ensure that . . . credit-based insurance scores are used 

appropriately and fairly to avoid negative effects.”4

 

The Commission has conducted a study addressing credit-based insurance scores 

in the context of automobile insurance. Pursuant to statutory directive, the FTC 

published two Federal Register Notices5

 soliciting comments from the public concerning 

methodology and research design. The Commission supplemented this information with 

numerous discussions between its staff and representatives of other government agencies, 

private companies, and community, civil rights, consumer, and housing groups. The 

public comments and information obtained in meetings with the various interested parties 

 

4

 15 U.S.C. § 1681 note (2006). 

5

 Public Comment on Data, Studies, or Other Evidence Related to the Effects of Credit Scores and CreditBased Insurance Scores on the Availability and Affordability of Financial Products, 70 Fed. Reg. 9652 

(Feb. 28, 2005); Public Comment on Methodology and Research Design for Conducting a Study of the 

Effects of Credit Scores and Credit-Based Insurance Scores on Availability and Affordability of Financial 

Products, 69 Fed. Reg. 34167 (June 18, 2004). 
7

provided essential information that allowed the Commission to complete this report. In 

addition, feedback from state regulators, industry participants, and the consumer, civil 

rights, and housing groups had a substantial impact on the methodology and scope of the 

analysis. 

This report discusses the information that the FTC considered, its analysis of that 

information, and its findings and conclusions. Parts I and II above present an Executive 

Summary and Introduction, respectively. Part III is an overview of the development and 

use of credit-based insurance scores, and Part IV discusses the relationship between 

credit history and risk. Part V addresses the effect of credit-based insurance scores on the 

price and availability of insurance. Part VI explores the impact of credit-based insurance 

scores on racial, ethnic, and other groups. Part VII describes the FTC’s efforts to develop 

a model that reduces differences for protected classes of consumers while continuing to 

effectively predict risk. Part VIII is a brief conclusion. 

III. DEVELOPMENT AND USE OF CREDIT-BASED INSURANCE SCORES

A. Background and Historical Experience

Consumers purchase insurance to protect themselves against the risk of suffering 

losses. They tend to be “risk averse,” that is, consumers would prefer the certainty of 

paying the expected value of a loss to the possibility of bearing the full amount of the 

loss. For example, assume that a driver faces a 1% risk of being in an automobile 

accident that would cause him or her to suffer a $10,000 loss, which means that the 

expected value of his or her loss is $100 (1% of $10,000). If the driver is risk averse, he 

or she would be willing to pay $100 or more to avoid the possible loss of $10,000. 
8

What makes insurance markets possible is that insurance companies do not 

simply take on the risk of their customers, they actually reduce risk. This does not mean 

that they reduce the total losses from car accidents or house fires, for example, but rather 

that they reduce the uncertainty that individuals face without themselves facing nearly the 

same amount of uncertainty. This is possible because the average loss on a large number 

of policies can be predicted much more accurately than the losses of a single driver or 

homeowner. For instance, while it is extremely difficult to predict who among a group of 

100,000 drivers will have an accident, it may be possible to predict the total number of 

accidents for these 100,000 drivers with a low margin of error.6

 By selling many policies 

that cover the possible losses for many consumers, an insurance company faces much 

lower uncertainty as to total losses than would each consumer if they did not purchase 

insurance. 

Insurance companies have a strong economic incentive to try to predict risk as 

accurately as possible. In a competitive market for insurance in which all firms have 

access to the same information about risk, competition for customers will force insurance 

companies to offer the lowest rates that cover the expected cost of each policy sold. If an 

insurance company is able to predict risk better than its competitors, it can identify 

consumers who currently are paying more than they should based on the risk they pose, 

and target these consumers by offering them a slightly lower price. Thus, developing and 

using better risk prediction methods is an important form of competition among insurance 

companies. 

 

6

 This risk reduction is due to the “law of large numbers.” Uncertainty is reduced as long as there is a 

sufficient degree of independence among the risk that individual consumers face. For example, selling 

flood insurance to those who live in a single flood plain reduces risks less than selling the policies to those 

who live in a broader geographic area. 
9

For decades, insurance companies have divided consumers into groups based on 

common characteristics which correlate with risk of loss. Automobile insurance 

companies divide consumers into groups based on factors such as age, gender, marital 

status, place of residence, and driving history, among others. Once insurance companies 

have separated consumers into groups based on these characteristics, they use the average 

risk of each of these groups in helping to determine the price to charge members of the 

group. 

Insurance companies report that during the last decade they have begun to use 

credit-based insurance scores to assist them in separating consumers into groups based on 

risk. Insurers have long used some credit history information when evaluating insurance 

applications, for example, considering bankruptcy in connection with offering 

homeowners insurance. In the early 1980s, insurance companies and others began 

assessing the utility of using additional information about credit history in assessing risk, 

leading to a more formal use of such information in a fairly simple manner by the early 

1990s.7

In the early 1990s, Fair Isaac Corporation (Fair Isaac), drawing on its experience 

developing credit scores, led the initial research to develop credit-based insurance scores. 

The company developed the first “modern” credit-based insurance score and made it 

available to insurance companies in 1993.8 This score was developed to predict the 

likelihood of claims being submitted for homeowners policies. Fair Isaac introduced a 

credit-based insurance score for automobile policies in 1995, and ChoicePoint introduced 

 

7

 Meeting between FTC staff and State Farm (July 13, 2004); Meeting between FTC staff and MetLife 

Home and Auto (July 12, 2004); Meeting between FTC staff and Allstate (June 23, 2004). 

8

 E-mail from Karlene Bowen, Fair Isaac, to Jesse Leary, Assistant Director, Division of Consumer 

Protection, Bureau of Economics (Jan. 30, 2006) (on file with FTC). 
10

a competing score at about the same time.9 These scores were developed to predict the 

loss ratios – claims paid out divided by premiums received – of automobile policies. 

Following the introduction of these third-party scores, some insurance companies began 

developing and using their own proprietary scores. 

Since the mid-1990s, the use of credit-based insurance scores has grown 

dramatically. According to industry sources, some of this growth is attributable to 

changes in technology and industry practices that have made it easier for companies to 

develop10 and use these scores.11 For example, during the 1990s insurance company 

actuaries began using advanced statistical techniques that made it easier to control for 

many predictive variables at the same time.12 This made it easier for them to develop 

proprietary scores and perhaps made them more receptive to using third-party scores. 

Insurers also explained that at this time they began combining more and more data from 

throughout their companies into integrated databases, and this “data warehousing” made 

it much easier for actuaries and others to engage in the research needed to develop 

scores.13

More fundamentally, however, insurance companies increasingly used creditbased insurance scores because their experience revealed that they were effective 

 

9 Id.; E-mail from John Wilson, ChoicePoint, to Jesse Leary, Assistant Director, Division of Consumer 

Protection, Bureau of Economics (June 13, 2005) (on file with FTC). 

10 Developing scores is a fairly expensive process, requiring significant information technology resources 

and technical expertise. It also requires a large amount of data on loss experience. Many smaller firms, 

and even some larger firms, therefore do not develop their own scores. See, e.g., Lamont Boyd, Fair Isaac 

Corporation, Remarks at the Fair Isaac Consumer Empowerment Forum (Sept. 2006) (noting only six firms 

use a proprietary scoring model). 

11 Industry participants estimate that of the firms that use credit-based risk scores, one-half (as measured by 

market share) use a proprietary score and one-half use a score that others developed. Among insurers who 

use a non-proprietary score, about two-thirds use a ChoicePoint score, and one-third use a Fair Isaac score. 12 These techniques are known as Generalized Linear Models (GLMs). GLMs make it easier to control for 

many predictive variables at once, and can be used to develop credit-based scoring models. GLMs play a 

central role in the analysis presented in this report, and are discussed in more detail in Appendix D. 13 Meeting between FTC staff and The Hartford (July 14, 2004). 
11

predictors of risk. For example, according to a published case study, in the early 1990s, 

Progressive entered the lower-risk portion of the automobile insurance market. 

Progressive used sophisticated risk prediction techniques that it had developed in its other 

lines of business to identify consumers who other insurers were overcharging relative to 

the risk they posed. Progressive offered these consumers the same coverage at a lower 

price, thereby persuading some of them to switch to Progressive.14 The success of 

Progressive’s strategy provided a powerful incentive for incumbent firms to improve 

their own risk prediction techniques to compete more effectively.15 Many of them 

responded to this incentive by increasing their development and use of credit-based 

insurance risk scores.16

Insurance companies now widely use credit-based insurance scores. Today, the 

fifteen largest automobile insurers (with a combined market share of 72% in 2005) all 

utilize these scores.17 Many smaller automobile insurers also use credit-based insurance 

scores.18

The development and increased use of credit-based insurance scores has been 

accompanied by concerns and criticisms about the validity of the underlying relationship 

between scores and risk and the fundamental fairness of using credit history information 

to make decisions about insurance. According to critics, credit-based insurance scores: 1) 

 

14 See, e.g., F. Frei, Innovation at Progressive (A): Pay as You Go Insurance, Harv. Bus. Sch. Case Study 

9-602-175 (Apr. 29, 2004). 

15 Incumbent firms had an incentive to use the new risk prediction technology in any case. The vigorous 

competition of Progressive, however, likely spurred incumbent firms to move more aggressively to use this 

technology than they otherwise would have. 

16 See id. 17 National Association of Insurance Commissioners, “Auto Insurance Database Report 2003/2004” (2006) 

(on file with the FTC); FTC staff reviews of websites and discussions with industry representatives. No 

market share data more recent than 2005 was available. 18 Fair Isaac Corporation states that it sells credit-based insurance scores to roughly 350 firms. Comment 

from Fair Isaac Corp. to FTC at 14 (Apr. 25, 2005), [hereinafter Fair Isaac Comment], available at

http://www.ftc.gov/os/comments/FACTA-implementscorestudy/514719-00090.pdf. 
12

unfairly penalize consumers who have suffered from medical or economic crises, or who 

have made perfectly legitimate financial decisions that are penalized by scoring models; 

2) affect consumers in arbitrary ways, because credit history information may contain 

errors; and, 3) have a negative impact on minority and low-income consumers.19

B. Development of Credit-Based Insurance Scores

According to score developers and insurance companies, credit-based insurance 

scores are developed in the same manner as credit scores generally. To construct a 

model, score developers obtain a sample of insurance policies for which losses are 

known. The period of time during which losses occurred or could have occurred is called 

the “exposure period.” Score developers start with the credit information available about 

customers at the beginning of the exposure period and the known losses for them during 

the period. Score developers then use various statistical and other techniques to develop 

a model that predicts losses based on the credit information that was available at the start 

of the exposure period. If the relationship between the credit information and loss is 

sufficiently stable over time, the model can be applied to the credit histories of other 

consumers to predict the risk of loss they pose. 

The details of the credit information used in particular models that produce creditbased insurance scores generally are not available. As emphasized above, insurance 

companies assert that risk prediction techniques are an important form of competition, so 

 

19 Hearing Before the New York State Assembly Comm. on. Ins. (Oct. 22, 2003) (statement of Birny 

Birnbaum, Executive Director, Center for Economic Justice). 
13

firms generally do not want to reveal the credit-based insurance scoring models they 

use.20 

Some states require by law that insurance companies make their models public. 

Insurance companies, however, explained that most insurance companies develop and use 

different scoring models in these states than they use in other states to minimize the 

competitive disadvantage elsewhere as a result of such mandated disclosures. An 

important exception is ChoicePoint, which has made its Attract Auto Scoring and other 

models available to the public. 

Based on the information the agency reviewed, a general picture of what data are 

used in credit-based insurance scoring model emerges.21 Table 1 presents examples of 

the types of information that often are used in models to predict credit-based insurance 

scores. Firms, however, vary significantly in the particular information they use in their 

models. For example, some insurance companies consider the type of credit granted, 

while others do not. Moreover, within a category of information, firms may consider 

different variables in calculating credit-based insurance scores. For instance, an 

insurance company may use the age of the oldest account in a credit report or may 

consider the average age of all accounts in the report. 

Insurance companies explained that they use credit-based insurance scoring 

models to predict the amount they will pay out in claims, i.e., claims risk. Some models 

simply predict the likelihood that a customer will file a claim. These models are most 

 

20 See Comment from National Association of Mutual Insurance Cos. to FTC at 2 (Apr. 25, 2005) 

[hereinafter NAMIC Comment], available at http://www.ftc.gov/os/comments/FACTAimplementscorestudy/514719- 00088.pdf. 21 Although credit-based insurance scoring models are developed to predict insurance claims, instead of 

credit behavior, many of the same types of information are used. A discussion of the factors that Fair Isaac 

Corporation uses in calculating its credit scores of consumers (“FICO scores”) is available at: 

http://www.myfico.com/CreditEducation/CreditInquiries. 
14

useful in those situations in which credit information is predictive of claim frequency, but 

not particularly predictive of the size of claims.22 

More commonly, however, models are used to predict the “loss ratio,”23 which is 

the amount that an insurance company pays out on claims divided by the amount that the 

customers pay in premiums. This has the advantage of controlling for the effects of noncredit factors on risk, such as age or driving history, as premiums are determined by those 

other factors. For any particular customer, the loss ratio usually will be either zero (i.e., 

no claims paid), or a number greater than one (i.e., claims paid in an amount that exceeds 

premiums received). In contrast, for a group of customers, the loss ratio typically will be 

a positive number less than one (i.e., some claims paid but in an amount that is less than 

total premiums received).24 If there is a strong relationship between customers with a 

particular credit-related attribute and historic loss ratios, this information can be used to 

predict the risk of loss associated with a prospective customer who shares that attribute.25

Other models are used to predict “pure premiums.” Pure premiums are the total 

amount that an insurance company pays on claims to consumers, not the amount that 

 

22 From a technical perspective, modeling frequency is relatively straight-forward. There are a number of 

standard multivariate techniques that can be used to estimate either the likelihood of a claim occurring, 

such as logistic regression, or the number of claims that would be expected during a period of time, such as 

Poisson regression. 

23 Loss ratios can be modeled in a variety of ways. Because loss ratios of individuals have such an oddlyshaped distribution B many zeros and some positive numbers that extend over a wide range B the modeling 

is not trivial, but it can be handled by GLMs. Loss ratios can also be modeled by decomposing the ratio 

and modeling the two components B claims paid and premiums B separately. For example, some 

ChoicePoint models use this technique. See e-mail from John Wilson to Jesse Leary, supra note 9. 24 Indeed, for an insurance company to be profitable, the amount that it pays out in claims must be less 

than the premiums it receives plus its return on investing those premiums. 25 MetLife has developed a rules-based system under which credit history information is used to sort 

potential customers based on their predicted loss ratio. MetLife’s “Personal Financial Management” uses 

combinations of various characteristics in an applicant’s credit report to assign the applicant to one of 

several risk categories without ever calculating a numerical score. This type of system essentially is a 

sophisticated analog to the simple rules-based approach sometimes used prior to the development of creditbased scores, under which, for example, some companies would not write homeowners policies to 

applicants with recent bankruptcies. 
15

customers pay in to the company. To build a credit-based insurance scoring model based 

on pure premiums, it is necessary to control for other risk variables and this can be done 

in one of two ways. One approach is to scale each consumer’s losses by an index of how 

risky they appear, based on other non-credit risk factors (e.g., age or driving history). 

This is analogous to the modeling of loss-ratios, with the non-credit-variable risk index 

playing the role of the premium, but avoids the complications that arise in loss ratio 

models if a credit score affected the premiums of the policies in the development 

database. 

The other approach involves treating credit history variables just like any other 

variable in predicting risk. One benefit of this approach is that it allows for certain credit 

history variables to have different effects on predicted risk for different groups of drivers. 

For example, the age of a consumer’s oldest account might be less predictive for young 

drivers than older drivers. Other credit characteristics might be very informative about 

drivers without prior claims or violations, but provide limited insight for drivers with 

poor driving records. Note that this approach may result in a model that does not produce 

a numerical score based solely on credit history information. 

C. Use of Credit-Based Insurance Scores

All insurance companies who use credit-based insurance scores explained that 

they do so in making decisions concerning potential customers. Insurance companies, 

however, also indicated that their use of scores in policy renewals for existing customers 

is much more varied and complicated. Some states limit the ability of insurance 

companies to use scores when customers renew policies. Even where not precluded by 

state law, some insurance companies decide not to use scores when customers renew 
16

policies to avoid damaging their relationship with these customers. Other states mandate 

that firms must use, or must use if the customer requests,26 updated credit-based insurance 

scores to modify premium rates. Even where not mandated by state law, some insurance 

companies use scores to modify premium rates for existing customers on request. In 

sum, insurance companies use credit-related insurance scores to assess premiums for 

potential customers and sometimes in determining premiums for existing customers who 

are renewing their policies. 

Insurance companies report that they use credit-based insurance scores in a 

variety of ways as part of the process of determining whether to offer insurance to 

prospective customers, and, if so, at what price. Making these determinations usually 

consists of two steps, referred to as “underwriting” and “rating.” In “underwriting,” 

insurance companies use certain characteristics of a consumer to assign him or her to a 

pool based on the consumer’s apparent risk of loss. The pool into which the consumer is 

placed sets the base premium rate for a policy, with the riskier pools having higher base 

premium rates. In “rating,” the second step, the insurance company uses other risk 

characteristics to adjust the base premium rate up or down to determine the actual amount 

the consumer would be charged.27

Some insurance companies said that they use credit characteristics in the 

underwriting step. For example, a firm might assign a potential customer to a risk pool 

based on the number of claims an applicant has filed in the past several years and the 

 

26 See, e.g., R. I. Ins. Regulation 25 § 11 (although requiring firms to recalculate a consumer’s score upon 

request every two years, firms generally can use a change in score only to lower premium rates), available

at http://www.dbr.state.ri.us/documents/rules/insurance/InsuranceRegulation25.pdf. 27 There has recently been some movement towards what can be called Acontinuous rating,” in which the 

risk for each applicant is evaluated and priced without first being assigned to a risk pool, but the two-step 

process is still standard. 
17

applicant’s credit-based insurance score. Using credit-based insurance scores in 

underwriting thus may affect the premiums that a potential customer would have to pay 

to obtain coverage, as the risk pool in which the consumer is placed determines his or her 

base premium rate. 

Other insurance companies report that they use scores in the rating step.28 A 

simple way to include scores is to determine a consumer’s base premium using non-credit 

factors, such as age or driving history, and then adjust that rate up or down in light of his 

or her score. A more complex method of using scores is to include credit as a rating 

factor when developing the entire rating scheme. Such an approach allows credit 

characteristics to be used interactively with other rating factors. Because how a creditbased insurance score predicts risk may vary with other rating variables, incorporating 

credit more fully into the rating step may assist in determining premiums that more 

accurately reflect risk.29

D. State Restrictions on Scores

 As of June 2006, forty-eight states have taken some form of legislative or 

regulatory action addressing the use of consumer credit information in insurance 

underwriting and rating; Pennsylvania and Vermont are the only states that have not 

regulated insurance scoring.30 Most of these laws and regulations are based on the 

 

28 While we are not aware that any insurance companies consider credit-based insurance scores at both the 

underwriting and rating stage, they could do so. 

29 An approach that is intermediate between having credit as an add-on or treating credit like any other 

rating factor is to make the size of a credit score discount or mark-up depend on other rating variables. For 

example, the good-credit discount for young single male drivers could be larger or smaller than the goodcredit discount for middle-aged married drivers. 30 The information in this section pertaining to state legislative and regulatory action addressing insurance 

scoring is from the National Association of Mutual Insurance Companies’ (NAMIC) 2004 survey of state 

laws governing insurance scoring practices. The report is available at: 

(continued) 
18

National Conference of Insurance Legislators’ (NCOIL) “Model Act Regarding Use of 

Credit Information in Personal Insurance,” which was released in 2002.31 

 The NCOIL Model Act prohibits insurers from using credit information as the 

sole basis for increasing rates or denying, canceling, or not renewing an insurance policy. 

The model also prohibits consumer reporting agencies from providing or selling 

information to others that was submitted to the agency pursuant to an insurance 

company’s inquiry about a consumer’s credit information, credit report, or insurance 

score. Further, the NCOIL model requires insurers to comply with five conditions: 

insurance companies must (1) notify an applicant for insurance if credit information will 

be used in underwriting or rating; (2) notify the applicant in the event of an adverse 

action based on credit information and explain its reasoning for the adverse action; (3) rewrite and re-rate a policyholder whose credit report was corrected; (4) indemnify 

insurance agents and brokers who obtained credit information or insurance scores 

according to an insurance company’s procedures and according to applicable laws and 

regulations; and (5) file its scoring models with the applicable state department of 

insurance.32 Twenty-seven states have adopted laws or regulations that adopt verbatim 

the language of the NCOIL model or incorporate restrictions that are very similar in 

scope and nature to those in the NCOIL model. 

 http://www.namic.org/reports/credithistory/credithistory.asp. The information in NAMIC’s survey has 

been updated to reflect newly enacted legislation and regulation through June 2006. Information on this 

new legislation and regulation is from NAMIC’s annual surveys of new state insurance laws and NAMIC’s 

2007 state law bulletins. The 2005 survey is available at: 

http://www.namic.org/reports/2005NewLaws/default.asp, the 2006 survey is available at: 

http://www.namic.org/reports/2006NewLaws/default.asp, and the 2007 state law bulletins are available at: 

http://www.namic.org/stateLaws/2007stateLawBulletins.asp. 31 A copy of the text of the NCOIL model is available at: http://www.assureusa.org/docs/NCOIL.doc. 32 In 2003, the National Association of Insurance Commissions described the NCOIL model in testimony 

before the U.S. House of Representative, Committee on Financial Services, Subcommittee on Financial 

Institutions and Consumer Credit. This testimony is available at: 

http://www.ins.state.ny.us/speeches/pdf/ty030610.pdf. 
19

In addition, twenty-one states have adopted some of the same types of restrictions 

included in the NCOIL model. Fifteen states prohibit certain uses of credit history 

information or ban the use of certain negative credit factors in the calculation of an 

insurance score. Eight states have adopted dispute resolution measures governing an 

insurance company’s responsibility to re-write and re-rate a policyholder whose credit 

report was corrected. Seven states require insurance companies to notify consumers that 

their credit information will be used in underwriting or rating. Twelve states require 

insurers to notify and explain to consumers any adverse action based on credit 

information. Seven states further require insurers to file their insurance scoring 

methodologies. 

There are several other types of restrictions that have been placed on the use of 

scores. Three states (Georgia, Illinois, and Utah) prohibit using credit history 

information as the sole basis in making underwriting or rating decisions. Oregon 

prohibits the use of credit history information to cancel or not renew existing customers 

or increase their rates, and Maryland bans the use of credit history when underwriting or 

rating existing customers. 

Finally, four states either have or had effective bans on the use of credit history 

information in underwriting or rating automobile insurance. Hawaii by statute 

specifically bans the use of credit information. California and Massachusetts effectively 

ban the use of scores through their rate regulation processes. Formerly, New Jersey had 

an effective ban in place, but the use of credit-based insurance scores is now allowed. 
20

IV. THE RELATIONSHIP BETWEEN CREDIT HISTORY AND RISK

Some prior researchers have studied the existence and nature of the relationship 

between credit history and insurance risk. To explore this relationship, the Commission 

conducted an analysis of a database of automobile insurance policies that the agency 

compiled for this study.33 A consistent finding of prior research and the FTC’s analysis is 

that credit information, specifically credit-based insurance scores, is predictive of the 

claims made under automobile policies. However, it is not clear what causes scores to be 

effective predictors of risk. 

A. Correlation between Credit History and Risk

1. Prior Research

As discussed above, risk prediction is an important method of competition among 

insurance firms. Research that insurance companies have conducted about the 

relationship between credit history and insurance risk therefore typically is proprietary 

and non-public. Nevertheless, several studies have been made public during the past 

decade that show a relationship between credit history and insurance risk. 

In 2000, James E. Monaghan, an actuary from MetLife Home and Auto, 

published a study analyzing the relationship between credit history variables and claims 

on automobile and homeowners insurance policies.34 He separately assessed a number of 

credit history variables, including delinquencies, inquiries, and debt utilization rates. 

Monaghan found that customers with the worst values for these variables posed a greater 

 

33 See section IV.A.2 and Appendix C for a description of the database. 

34 James N. Monaghan, The Impact of Personal Credit History on Loss Performance in Personal Lines, 

Casualty Actuarial Society Ratemaking Discussion Paper (2000) (presented at the Winter 2000 CAS 

forum), available at http://www.casact.org/pubs/forum/00wforum/00wf079.pdf 
21

risk (as measured by loss ratios) than customers with the best values - often roughly 50% 

more for automobile policies and over 90% more for homeowners policies.35 He found 

the same pattern of increased risks when he conducted his analysis controlling for other 

non-credit risk factors one-by-one. 

After this research, several insurance industry trade associations hired EPIC 

Actuaries (EPIC) to construct a database of automobile policies with information from a 

number of different insurers.36 EPIC analyzed the link between credit history and risk, 

and described its results in a report issued in 2003.37 EPIC reported the relationship 

between credit scores and different measures of risk. The study showed a strong 

relationship between credit-based insurance scores and the frequency with which claims 

were made, as well as between scores and the total dollar amount insurance companies 

paid on these claims.38 It also showed: (1) no correlation between scores and the size of 

liability coverage claims; (2) a weak correlation between scores and the size of collision 

coverage claims; and (3) a strong correlation between scores and the size of 

comprehensive coverage claims. 

In 2003, researchers at the Bureau of Business Research (BBR) at McCombs 

School of Business at the University of Texas used data from five automobile insurance 

companies in Texas to study the relationship between credit-based insurance scores and 

 

35 As discussed in the section on the development of credit scores, the loss ratio can be used to control for 

the effects of the variables used to determine premiums. However, this relies on the assumption that the 

premiums accurately reflect the risks associated with those variables. 36 The automobile policy data that form the core of the database that we used to conduct our analysis for 

this report are a subset of the data collected for use in the EPIC report. That database is discussed in more 

detail below, and in Appendix C. 

37 Michael J. Miller and Richard A. Smith, The Relationship of Credit-Based Insurance Scores to Private 

Passenger Automobile Insurance Loss Propensity: An Actuarial Study by EPIC Actuaries, LLC (June 

2003) [hereinafter EPIC Study], available at http://www.progressive.com/shop/EPIC_CreditScores.pdf. 38 EPIC also conducted a multivariate analysis that included controls for most non-credit risk variables 

used to underwrite and rate automobile polices. While the relationship between scores and the total amount 

paid out on claims was not as large once controls were included, it remained quite strong. 
22

losses. The BBR researchers found that customers with lower scores were more likely to 

file claims under their automobile insurance policies than customers with higher 

insurance scores. In addition, the researchers reported that customers with lower scores 

filed claims for larger dollar amounts than customers with higher scores.39 To control for 

the effects of non-credit risk factors, the BBR researchers used an analysis of loss ratios, 

and found that loss ratios were higher for customers with lower scores than for customers 

with higher scores.40

In 2004, the Texas legislature directed the Texas Department of Insurance (TDI) 

to conduct a study and issue a report addressing the relationship between credit-based 

insurance scores and risk for automobile and homeowner policies. In reports issued in 

late 2004 and early 2005,41 TDI analyzed data from six large insurance firms operating in 

Texas, using each company’s credit scoring model.42 For automobile policies, it found 

that scores were negatively correlated with total dollars of claims, i.e., as the scores of 

customers increased, the total amount that the insurance companies paid out in claims 

decreased. Insurance companies paid out less on automobile policies for customers with 

higher scores because they filed fewer claims than customers with lower scores.43 For 

homeowners insurance, TDI found similar results. TDI found that scores were negatively 

 

39 Bureau of Business Research, McCombs School of Business, The University of Texas at Austin, “A 

Statistical Analysis of the Relationship Between Credit History and Insurance Loss” (Mar. 2003). The 

report does not make clear which particular types of automobile coverage were studied. 

40 Id. 41 Texas Department of Insurance, “Use of Credit Information by Insurers in Texas: The Multivariate 

Analysis” (Jan. 31, 2005) (supplemental report) [hereinafter 2005 Texas Report]; Texas Department of 

Insurance, “Use of Credit Information by Insurers in Texas” (Dec. 30, 2004) [hereinafter 2004 Texas 

Report]. 

42 All six insurance companies provided TDI with data on automobile policies, and three of them provided 

data on homeowners policies. 

43 TDI’s findings with regard to automobile policies were consistent regardless of whether it controlled for 

other risk factors in its analysis. 
23

correlated with both total dollars of claims and loss ratios, i.e., as the scores of customers 

increased, the total amount that insurance companies paid out on their policies decreased. 

2. Commission Research

a. FTC Database 

The FTC undertook an analysis to determine the relationship between credit 

history and risk of loss. Five of the firms that provided automobile insurance policy data 

for the EPIC study described above provided the same information for the Commission’s 

study.44 This information included policy and driver characteristics, claims, and a 

ChoicePoint Attract Standard Auto credit-based insurance score for the customer who is 

named first on the policy. The information submitted to the Commission related to 

automobile insurance policies in place at any time between July 1, 2000, and June 30, 

2001. 

The FTC combined this information from insurance companies with data from a 

number of other sources to create its database. The agency included additional 

information in the database to broaden the range of credit history variables analyzed; to 

improve the set of other risk controls in the analysis; to provide an independent measure 

of claims; and to analyze issues relating to race, ethnicity, income, and national origin.45 

One important feature of the FTC database was that we created weights to make it 

 

44 The five firms together represented 27% of the automobile insurance market in 2000. The data were 

drawn in a way that ensured a nationwide representation of policies. More information about the 

companies and the database are provided in Appendix C. A discussion of the limitations of the database 

and of our analysis is presented in Appendix F. 

45 We obtained Fair Isaac credit-based insurance scores for a sub-sample of the people in the database. All 

of the results presented in the body of the report are for the ChoicePoint Attract score. All of the analysis 

was also conducted using the Fair Isaac score. The results were qualitatively similar regardless of whether 

the ChoicePoint or the Fair Isaac score was used. Descriptions of all “robustness checks” and other 

variations of the analysis are presented in Appendix F. 
24

representative of car owners, by neighborhood income and race and ethnicity, throughout 

the United States.46 A more detailed description of the construction and contents of the 

FTC database is provided in Appendix C. 

In assessing the relationship between credit history and risk, the FTC focused its 

analysis on four major types of coverage included in automobile policies: property 

damage liability coverage, bodily injury liability coverage, collision coverage, and 

comprehensive coverage.4748 Property damage liability coverage insures the customer 

against liability for damage he or she causes to the cars and other property of others. 

Bodily injury liability coverage protects the customer from liability for bodily injuries he 

or she causes to others. Collision coverage insures the customer against damage to his or 

her own car from collision or rollover. Comprehensive coverage protects the customer 

against losses from theft of his or her own car and for damage to the car other than from 

collision or rollover (e.g., vandalism, fire, hail, etc.). 

The FTC first analyzed the simple relationship between credit-based insurance 

scores and claims for these four coverages. Table 2 shows, for each coverage and for 

each score decile, the average number of claims per year of coverage (per hundred cars, 

to show detailed differences across deciles), the average size of claims, and the average 

total amount paid out on claims per year of coverage (which is the product of the number 

of claims and the average size of claims). 

 

46 The weighting also makes the data representative by geographic area. See Appendix D for a discussion 

of the development of the weights. 

47 The FTC database also contains information on two first-party medical coverages, usually referred to as 

MedPay and personal injury protection, or “PIP.” Claims on these policies are relatively infrequent, and 

the coverages vary from state to state. For these reasons, we do not focus our analysis on these coverages. 48 These definitions come from the Insurance Information Institute, and are available in more detail at: 

http://www.iii.org/individuals/auto/a/basic/. 
25

Figure 1 presents graphs of the relationship between scores and the average total 

amount paid out on claims. In Figure 1, the horizontal axis shows automobile drivers 

grouped into ten equal groups (“deciles”) based on their credit-based insurance score,49

with drivers in the decile with lowest scores located at the far left and drivers in the decile 

with the highest scores at the far right. The vertical axis measures the average dollars 

paid out on claims per year. This measure of risk is calculated relative to drivers with the 

highest credit-based insurance scores, which means that the value of the highest-score 

group (i.e., those in the tenth decile) has been defined as one. 

Figure 1 shows that there is a relationship between credit-based insurance scores 

and risk for all four types of coverage analyzed. Specifically, the downward slopes of the 

darker (higher) lines in Figure 1 show that as scores increase, the risk of loss consistently 

decreases. (These lines were produced simply by graphing the average total paid on 

claims – column (c) – from Table 2, relative to the highest score decile.) They show, for 

example, that insurance companies paid out nearly twice as much on the property damage 

liability policies of customers in the group with the lowest scores (i.e., those in the first 

decile) as they did for the group with the highest scores (i.e., those in the tenth decile). 

Credit-based insurance scores thus are predictive of the amount that insurance companies 

pay in claims to consumers. 

The FTC then constructed statistical models of insurance claims. These models 

produce estimates of the relationship between scores and claims, and allow us to control 

for the effects of other risk variables. 

 

49 Score is measured by deciles because the units of scores are arbitrary, so there is no reason to believe that 

the relationship between changes in score and changes in risk is constant across the score distribution. For 

example, going from a score of 600 to 620 may have a different effect on predicted risk than going from 

800 to 820. 
26

The lighter (lower) lines in Figure 1 show the relationship between credit-based 

insurance scores and the amount paid out after controlling for other standard risk factors, 

such as age and driving history.50 The slope of each line demonstrates that the 

relationship between scores and risk persists when controls for other risk variables are 

included, although the relationship is less strong. Once controls are included, for 

instance, the amount that insurance companies paid out on property damage liability 

claims to customers with the lowest credit-based insurance scores was 1.7 times the 

amount they paid to customers with the highest credit-based insurance scores, down from 

paying nearly twice as much if no controls are included. Because the relationship is less 

strong when other variables are included, customers who appear more risky based on 

non-credit variables are also more likely to have lower credit scores. Nevertheless, even 

when non-credit variables are included in the analysis, credit-based insurance scores 

continue to predict the amount that insurance companies are likely to pay out in claims to 

consumers. 

Figure 1 therefore shows that there is a relationship between credit-based 

insurance scores and the total dollar amount of claims that insurance companies paid. To 

refine this analysis, the FTC assessed whether customers with the lowest scores were 

likely to cause insurance companies to pay out more because the customers file more 

claims, file claims for higher amounts, or both. As shown by the darker (higher) lines in 

Figure 2, customers with lower scores filed substantially more claims than those with 

 

50 These other factors are controlled for by estimating a Tweedie GLM model of total dollars of claims 

using score deciles and all of the other risk factors. Modeling details and the other variables included in the 

models are discussed in Appendix C. Race, ethnicity, and income are not included at this stage of the 

analysis. 
27

higher scores.51 For instance, customers with the lowest credit-based insurance scores 

were about 1.7 times more likely to file a property damage liability claim as customers 

with the highest credit-based insurance scores. On the other hand, as shown in the lighter 

(lower) lines in Figure 2, the average size of the claims paid was nearly constant 

regardless of credit-based insurance score. The one exception is comprehensive 

coverage, which does show a relationship between claim size and score. The different 

result for comprehensive coverage may be attributable to a correlation between having a 

lower score and a higher probability of being a victim of automobile theft, because theft 

claims are larger than claims resulting from most other events that this type of insurance 

covers. 

The underlying claims data presented in Table 2 (which are simple averages 

without controls for other risk factors) show the same patterns as those in Figures 1 and 

2, and provide additional information on the absolute size of claims risk for different 

coverages and different score deciles. One important point that comes out in Table 2 is 

the difficulty of predicting the claims of individual customers. While the average number 

of claims per year in the lowest score decile of collision coverage, for example, was more 

than twice that in the highest decile, there were still only 12 claims per hundred cars per 

year of coverage for the lowest score decile. So, the vast majority of customers in even 

the riskiest decile would not file a claim in a given year. As with other risk variables, 

credit-based insurance scores are able to separate consumers into groups with different 

average risk, but cannot predict the claims of individual consumers.

 

51 The results for the frequency and severity of claims come from models that include controls for other risk 

variables. Modeling details and the other variables included in the models are discussed in Appendix C. 
28

 b. Other Data Sources

In addition to this analysis of the information in the FTC database, the 

Commission evaluated alternative and independent information to assess the relationship 

between credit-based insurance scores and risk. ChoicePoint Inc. collects data on claims 

from most major automobile insurance firms in the United States. The data allow 

insurance companies to learn whether a potential new customer has filed a claim under a 

previous policy with another firm, and then use that information in underwriting and 

rating. ChoicePoint refers to this data set as the Comprehensive Loss Underwriting 

Exchange (“CLUE”). 

We obtained the CLUE reports for each person in the FTC database for the period 

July 1995 – June 2003. This encompasses three time periods: (1) the five years prior to 

the period of the firm-submitted data; (2) the period of the firm-submitted data (July 2000 

– June 2001); and (3) the two-year period following the period of the firm-submitted 

data. The data on claims prior to the firm-submitted data (i.e., prior to July 2000) were 

used to construct controls in the risk models that the FTC ran.52 The CLUE data also give 

us an alternative and independent source of data on claims to use to measure the 

relationship between credit-based insurance scores and claims. 

Figure 3 shows the average dollars paid out for each decile on policies for each of 

the four main coverages studied.53 Each panel includes average claims for three data 

 

52 We used three years of prior claims data to construct the risk variables used in the risk models. The use 

of information on prior claims is an improvement over previously published analyses of credit-based 

insurance scores, which have not included controls for prior claims filed on policies with consumers’ prior 

insurers. 

53 The results in Figures 1 and 2 are for a stratified sub-sample of the database. The stratification was based 

on which policies had claims in the company-provided data. The sub-sample is discussed in Appendix C. 

The results in Figure 3 are for the entire sample of 1.4 million policies. We use the full sample because the 

stratified sub-sample does not have sufficient information to reliably measure claims in the CLUE data for 

the six-month period starting July 1, 2001. The results shown on these graphs are not controlled for other 

(continued) 
29

sources and samples: (1) claims in the data set we received from the firms; (2) claims in 

CLUE for the year over-lapping with the company data set (July 2000 B June 2001); and, 

(3) claims in CLUE for the six-month period following the company data set (July 2001 B

December 2001).54

These results show a consistent pattern of average total dollars paid out on claims 

being higher for individuals with lower credit-based insurance scores. The relationship is 

generally similar across the data sources for the year of overlap, with the exception that it 

is somewhat weaker for bodily injury liability coverage. 55 For the six months starting 

July 1, 2001, the results vary for different types of automobile insurance coverage. 

Comprehensive coverage results look very similar in the two time periods. The overall 

slope is similar for bodily injury but the relationship is less stable. The relationship 

becomes much flatter in the later time period for collision coverage, and somewhat flatter 

for property damage liability. This may be evidence that credit-based insurance scores 

become less predictive of claims for these coverages as more time passes from when the 

scores were calculated. 

 non-credit risk variables, because we do not have reliable information about those variables outside of the 

time period covered by the company data and because CLUE does not contain information at the car level. 

For the same reasons, we use the sum of the earned car years for each coverage on each policy when 

analyzing the CLUE data. 

54 We used a six-month period because we were concerned that information on the number of insured 

vehicles and coverage choices would become less reliable the further in time the data were from the data 

that the companies provided. We also measured claims for the six-month period starting July 1, 2001, for a 

sample of drivers limited to those who did not have any claims during the period covered by the companyprovided data. This gave results for that time period that were very similar to the results for the full sample 

for that same time period. 

55 Given the time it can take for the full cost of bodily injury liability claims to be determined, this may 

affect how claims for bodily injury coverage are reported to the CLUE database. 
30

B. Potential Causal Link between Scores and Risk 

Thus, two different data sets, and previously published research, show that creditbased insurance scores are correlated with the total amount that insurance companies pay 

out on claims under automobile insurance policies.56 The question that naturally arises is 

why a customer’s credit history makes it more or less likely that he or she will suffer a 

loss and file an insurance claim. The FTC considered various proposed explanations of 

such a link and the data available bearing on those explanations. The information 

available, however, does not allow the agency to draw any broad or definitive 

explanations why there is a relationship between credit-based insurance scores and risk. 

 We emphasize that assessing the relationship between credit history and 

insurance risk necessarily involves addressing the attributes and circumstances on 

average of consumers with particular levels of credit-based insurance scores. Of course, 

these attributes and circumstances do not necessarily apply to each consumer with a 

particular level of score. People may have negative information on their credit histories 

for reasons that would seem to be totally unrelated to insurance risk. The starkest 

example is when the information is simply incorrect. Consumers also may wind up in 

financial distress for all sorts of reasons that have no bearing on how risky they are as 

drivers.57 In addition, consumers may have credit histories that lead to low scores 

because of a lack of an extensive credit history. This may reflect societal effects like a 

lack of mainstream credit offerings where a consumer lives, or a lack of sophistication 

 

56 Section VII of this report contains the results of the FTC’s successful efforts to build scoring models that 

are predictive of risk. The FTC’s scoring model predicts risk in the company-provided claims data, and in 

the CLUE data for an entirely different set of people and a different time period. These results provide 

additional evidence that credit history information can be used to predict automobile insurance claims. 

57 Hearing Before the New York State Assembly of Comm. on Ins. (Oct. 22, 2003) (statement of Birny 

Birnbaum, Executive Director, Center for Economic Justice). 
31

about mainstream credit markets. Again, it is not apparent that these types of 

circumstances should lead to higher insurance risk. 

A strong credit history, however, might indicate that a consumer has taken care in 

managing his or her financial affairs B avoiding loans that might be difficult to repay, 

avoiding high balances on credit cards, making sure that bills are not misplaced and are 

paid on time, etc. A consumer who is prudent in financial matters may also be cautious 

in other matters related to insurance, such as being more likely to put time, effort, and 

money into things like car and home maintenance, cautious driving habits, etc. An 

overall inclination to be prudent may lead a consumer both to have a strong credit history 

and file fewer insurance claims. 

There is ongoing research reflected in the behavioral economics literature that 

tends to show that people who engage in risky behavior in an area of their lives are often 

willing to take on more risk in other areas, as well. Researchers have studied attitudes 

toward risk, as well as behavior, in financial settings and driving, as well as a range of 

other areas including smoking, occupational choice, and migration.58 One recent article 

argues that existing research shows that physiological and psychological factors affect 

how much risk individuals are willing to take in their financial, driving, and other 

behavior. Many of the psychological studies surveyed in that article analyze the 

relationship between psychological factors and risk-taking in a single aspect of life. The 

authors connect these results between financial behavior and driving from studies on 

separate groups of people, and posit the theory that credit-based insurance scoring works 

 

58 See, e.g., Thomas Dohmen, et al., Individual Risk Attitudes: New Evidence from a Large, Representative, 

Experimentally-Validated Survey (Sept. 2005), available at http://ftp.iza.org/dp1730.pdf. 
32

because scores reflect the psychological makeup of the individual in ways that affect 

insurance risk.59 

Others have suggested that credit history provides information about a consumer’s 

circumstances and those circumstances affect the likelihood or size of claims. One 

example is that a driver with a low credit-based insurance score may be in a distressed 

financial situation. This may cause stress that makes the consumer a less attentive 

driver.60 Being in a distressed financial situation also might give the driver a greater 

incentive to try to obtain payment under an insurance policy. For example, he or she may 

be more likely to file a claim for a small amount of damage to an automobile rather than 

paying for those expenses out of pocket. 

Another circumstance that could explain a correlation between credit-based 

insurance scores and risk of loss under automobile insurance policies is differences in the 

number of miles driven. The number of miles that a car is driven is directly related to 

automobile insurance risk, but companies find it difficult to capture information on 

“miles driven” with a great deal of accuracy. Consumers with lower scores may put more 

miles on their cars than consumers with higher scores. For example, consumers with 

lower scores may put more miles on their cars because they have more drivers per car in 

their household, they share cars with others, etc. If there is a link between credit-based 

insurance scores and number of miles driven, this could lead to a correlation between 

credit-based insurance scores and risk.61

 

59 Patrick L. Brackett and Linda L. Golden, Biological and Psychobehavioral Correlates of Risk Scores and 

Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works, 74 J. OF RISK AND 

INS. 23 (2007). 60 Id. 61 See, e.g., Patrick Butler, Driver Negligence vs. Odometer Miles: Rival Theories to Explain 12 

Predictors of Auto Insurance Claims (Aug. 9, 2006) (presented at the American Risk & Insurance 

(continued) 
33

As discussed above, a circumstance that could explain the relationship between 

credit-based insurance scores and risk under automobile insurance policies is differences 

in the resources that consumers put into maintaining their cars. Consumers with lower 

scores may not be willing or able to spend as much money to maintain their cars. This 

may, in turn, make the cars more dangerous to operate and lead to more or larger claims. 

If this were an important part of the explanation for the relationship between scores and 

risk, one would expect the relationship to be weaker for newer cars, which presumably 

would not have had the chance to develop maintenance-related safety problems. 

The FTC used its database to test this hypothesis. We divided cars in our 

database into three groups: model years 1992 and older, model years 1993 – 1996, and 

model years 1997 and later. Using policy information from 2000 to 2001, we estimated 

the relationship between credit-based insurance scores and property damage liability risk 

separately for these three groups.62 Figure 4 shows that credit-based insurance scores are 

strongly correlated with risk for each group, that is, the slope of the lines reveal that 

within each of the three model-year categories, consumers with lower scores pose a 

greater risk of loss than consumers with higher scores. 

The relationship between credit-based insurance scores and risk was slightly 

stronger for the oldest cars. For the oldest cars, consumers with the lowest scores are 

1.81 times riskier than consumers with the highest scores. By contrast, for the newest 

cars, consumers with the lowest scores are 1.68 times riskier than consumers with the 

highest scores, and for middle-aged cars, consumers with the lowest scores are 1.64 times 

 Association Annual Meeting), available at http://www.aria.org/meetings/2006papers/butler.pdf. 62 We used property damage liability because (unlike collision or comprehensive coverage) the size of 

claims does not depend on the value of the car covered by the policy. Car values will vary with model 

year, so using coverages where the size of claims varies with the value of the car would complicate the 

analysis. 
34

riskier than consumers with the highest scores. Our results are weakly consistent with the 

hypothesis that some of the relationship is attributable to consumers with lower scores 

spending less to maintain their vehicles, but also show that difference in maintenance is 

not the primary cause of the relationship. 

In short, many explanations have been offered as to why the characteristics or 

circumstances of consumers might account for the relationship between scores and risk. 

Little empirical data testing these possible explanations are available. The FTC tested 

one possible explanation for the relationship between scores and risk under automobile 

policies, and the results were weakly consistent with the hypothesis that some of the 

relationship could be attributable to the lower amount that consumers with lower scores 

may spend on maintenance. Although this result provides some insight, the information 

available does not allow the agency to draw any broad or definitive conclusions as to the 

reason that there is a relationship between scores and risk. 

V. EFFECT OF CREDIT-BASED INSURANCE SCORES ON PRICE AND 

AVAILABILITY

 Credit-based insurance scores are predictive of risk for automobile policies. 

Insurance companies therefore are able to use these scores to underwrite and rate policies 

in ways that correspond more closely to individual risk, on average. Enhanced accuracy 

results in decreased premiums for lower-risk consumers and in increased premiums for 

higher-risk consumers, and reduces the extent to which lower-risk consumers subsidize 

higher-risk consumers. Enhanced accuracy also may have broader effects in the 

marketplace. It may make insurance companies willing to offer policies to consumers 

posing a wider range of risk and it may reduce adverse selection among consumers. 
35

 A. Credit-Based Insurance Scores and Cross-Subsidization

Every insurance policy written for a consumer can be thought of as posing a true 

level of claims risk, that is, the expected cost to the insurance company of claims that the 

customer will submit. If the firm knew this true level of risk, it could base premiums on 

this risk. Because of practical limitations on the ability of firms to obtain and process 

information, they cannot determine the true level of risk that any particular consumer 

poses.63 Instead, they must use the information available to them to estimate the expected 

claims cost for each consumer. Traditionally, insurance companies have divided 

customers into groups based on their characteristics and calculated expected average 

losses for the group, after which group members are charged premiums based on these 

expected losses. 

Because the true expected claims costs will vary within any group of customers, 

some in the group will be paying premiums that are higher and others will be paying 

premiums that are lower than their own individual true expected claims cost. Those in 

the group with lower expected claims costs (i.e., the lower-risk customers) subsidize 

those with the higher expected claims cost (i.e., the higher-risk customers).64 In the 

absence of perfect information about individual customer risks, there will always be some 

consumers in an insured group who subsidize other consumers in the group.65 

 

63 Because insurers never have complete information about consumers, their estimates of expected claims 

costs are, at best, only correct on average; some estimates are over-estimates and others under-estimates. 

Such a situation is referred to as “imperfect information” about consumer risk. 

64 This is ex ante cross-subsidization (or, cross-subsidization “in expectation”). It is a distinct concept from 

ex post cross-subsidization. Inherent in the concept of insurance is ex post cross-subsidization, that is, 

customers who do not experience loss subsidize customers who do. 65 Note that if information is symmetric between insurers and consumers (i.e., they both have the same 

imperfect beliefs about expected claims costs), consumers will not know whether they are beneficiaries or 

contributors to the subsidization. Given that consumers do not know whether they are paying more or less 

(continued) 
36

Better risk prediction techniques allow insurance companies to more effectively 

separate higher-risk consumers from lower-risk consumers. This information assists 

insurance companies in charging consumers prices that correspond more closely to the 

true risk they pose, on average. This, in turn, decreases the premiums of lower-risk 

consumers and increases the premiums of higher-risk consumers, on average. Improved 

risk prediction techniques therefore reduce the extent to which lower-risk consumers 

subsidize higher-risk consumers.66

Even though improved risk prediction techniques will make firms’ estimates of 

the riskiness of consumers on average more accurate, the predicted risk of some 

individual consumers may become less accurate. For example, there are some consumers 

who are very safe drivers but have low credit-based insurance scores. If scores are used, 

the predicted risk for these specific individuals will become less accurate. This result is 

unavoidable in any scheme used to make predictions about the risk consumers pose. 

Therefore, even if risk predictions become more accurate overall as additional predictive 

information is considered, there will always be some people who are much safer – or 

much riskier – than they appear. 

The FTC analyzed the information in its automobile insurance database to 

estimate the extent to which the use of credit-based insurance scores (a risk prediction 

technique) could reduce cross-subsidization. Many of the premiums for policies included 

in the database were calculated without using scores, 67 and the data do not indicate which 

 than their true risk, their decisions will be unaffected by the existence of cross-subsidization. 

66 This is true even for customers of firms that do not adopt the more accurate prediction method, because 

those firms will wind up with a riskier and more homogenous pool of customers. Because the pool of 

customers is more homogenous, there will be less cross-subsidization within that group of consumers. 67 E-mail from Rick Smith, Towers Perrin, to Jesse Leary, Assistant Director, Division of Consumer 

Protection, Bureau of Economics (Apr. 13, 2005) (on file with FTC). 
37

policies these were. The FTC database contains information from 2000-2001, shortly 

after the introduction of scores. As discussed above, scores typically are used in 

determining the premiums to be charged to prospective customers. Customers who 

renewed their policies during 2000-2001 thus were not likely to have had scores used to 

determine their premiums. In addition, although by 2000 insurance companies were 

using scores to determine premiums in many states, their use was not universal. 

Accordingly, many, and probably most, of the premiums charged to consumers during 

this period of time were determined without the use of credit-based insurance scores. 

Because most of the premiums in the database likely do not reflect the use of 

credit-based insurance scores, the FTC used risk, measured in expected total dollars of 

claims, as a substitute for premiums in an analysis of the effects of scores. We believe 

that this calculation of risk is a reasonable substitute for premiums in this context, 

because the premiums that an insurance company charges consumers in a competitive 

marketplace should be roughly proportional to the risk they appear to pose.68

The FTC used a three step analysis to evaluate how expected risk changes if 

insurance companies consider credit-based insurance scores. The first step was to use a 

model to calculate a predicted dollar risk for each consumer using all risk factors in the 

database, except score.69 The second step was to calculate a predicted dollar risk for each 

consumer using all risk factors plus a score. Both of these steps to calculate predicted 

 

68 Some industry participants have stated that homeowners and automobile insurance markets are fiercely 

competitive. See, e.g., Comment from State Farm Ins. Co. to FTC at 3-4 (Apr. 25, 2003) [hereinafter State 

Farm Comment], available at http://www.ftc.gov/os/comments/FACTA-implementscorestudy/514719-

00100.pdf; Comment from the American Ins. Ass’n to FTC at 14 (Apr. 25, 2005) [hereinafter AIA 

Comment], available at http://www.ftc.gov/os/comments/FACTA-implementscorestudy/514719-

00084.pdf. 69 This was done using a Tweedie GLM model. Modeling details are provided in Appendix C. Race, 

ethnicity, and income were not considered at this stage of the analysis. 
38

dollar risk were conducted separately for property damage liability, bodily injury 

liability, collision, and comprehensive coverage.70 The third and final step was to sum 

the predicted dollar risks for all four types of insurance coverage with and without the use 

of credit-based insurance scores.71 This produced two estimates of total risk for each 

insurance policy in the database: an estimate without using a score, and an estimate using 

a score. 

The FTC’s analysis predicts that the use of credit-based insurance scores 

redistributes premium costs from consumers with higher scores to those with lower 

scores.72 This is a zero-sum calculation: the total increases in premiums predicted if 

scores are used must be exactly the same as the total decreases in premiums predicted. 

Figure 5 shows the results of the FTC’s analysis of the effect of credit-based 

insurance scores on changes in premiums. It shows what share of consumers would be 

predicted to have changes of different sizes. Figure 5 also reveals that if credit-based 

insurance scores are used, more consumers (59%) would be predicted to have a decrease 

in their premiums than an increase (41%). 

 

70 We also conducted this analysis using a single-equation model of all coverages, instead of separate 

models by coverage. As discussed in Appendix F, this yielded similar results. 

71 This approach uses the actual coverage choices of individuals. That is, we predict claims cost for 

individuals only for the coverages they had, and measure the change in their total predicted claims for those 

coverages. This has the advantage of taking into account the real choices people made when purchasing 

insurance, but the disadvantage of not allowing for the possibility that individuals would change their 

coverage choices in response to changing premiums. To generate the overall distribution of changes, we 

weighted consumers by the earned car years on their property damage liability coverage. 

72 We emphasize that this is not a measure of how firms are actually using scores to price consumers. 

Scores are not used to underwrite or rate all customers, especially existing customers. In addition, firms 

may not adjust premiums in response to scores as much as our analysis would predict. For these two 

reasons, this exercise may overstate the redistributive effects of using scores. The fundamental assumption 

of the analysis, that premiums will be proportional to predicted risk, is likely to be violated in the shortterm, especially if existing customers are not fully re-underwritten and re-rated every year. In sum, this 

approach probably overstates the redistributive effects to date of using scores, but should be a reasonable 

substitute for the long-term effects of using scores. 
39

The increased premiums for consumers whose premiums would rise are larger 

than the decreased premiums for those whose premiums would fall. This can be seen in 

the longer “tail” on the right-hand side of the graph, which shows larger changes in the 

direction of an increase. The median increase for those with an increase in predicted risk 

is 16% (i.e., one-half the increases in predicted risk are greater than 16% and one-half are 

less than 16%), while the median decrease is 13%. 

1. Possible Impact on Car Ownership

If using credit-based insurance scores results in consumers paying premiums that 

are closer to the true risk that they pose, this could result in car owners incurring costs 

closer to the real costs of owning and operating their cars. Internalizing these costs could 

affect consumer decisions whether to own cars, thus resulting in more efficient car 

ownership. 

If consumers decide how many cars to own based on the benefits and costs of car 

ownership, their decisions can be said to be “efficient” in that they will choose to own 

cars only when the benefits are at least as great as the costs. If consumers pay premiums 

that are lower than the risk they actually pose, they will own more cars than is efficient, 

because other people are helping to pay for the cost of their driving. 73 And, if consumers 

pay premiums that are higher than the risk they pose, they will own fewer cars than 

would be efficient, because they face costs higher than the true total costs of their driving. 

The use of credit-based insurance scores to charge premiums that more accurately reflect 

 

73 This is a classic “negative externality.” Negative externalities arise any time consumers or businesses 

pay costs for a product that are less than the costs to society. Because societal costs are not considered in 

the decision of consumers or businesses, their decisions will be inefficient for society. 
40

the true cost of driving, on average, thus could lead to a more efficient level of car 

ownership. 

The FTC was not able to determine whether and to what extent credit-based 

insurance scores have an effect on automobile ownership. We are not aware of 

information addressing specifically how much of an increase or decrease in the cost of 

driving will cause consumers to decide whether to own a vehicle.74 Moreover, even if we 

were able to determine the effect of insurance scores on car ownership, this study does 

not assess whether such an outcome would be equitable. 

2. Possible Impact on Uninsured Driving

Using credit-based risk scores to determine premiums also could have an effect on 

the number of drivers who drive without insurance. Although most states have 

requirements that drivers carry specified minimum amounts of liability insurance, there 

are still significant numbers of drivers who drive without insurance. Raising premiums 

of drivers with lower scores could lead to more of them driving without insurance. 

Lowering premiums of drivers with higher scores could lead to fewer of them driving 

without insurance. Whether the use of scores on balance leads to more or fewer people 

driving without insurance depends on which of these two effects is greater.75

 

74 Even though there is published work on the effects of prices on new car sales, see, e.g., Patrick S. 

McCarthy, Market Price and Income Elasticity of New Vehicle Demands, 78 REV. OF ECON. AND STATS. 

543 (1996), we are not aware of studies that measure the effect of the cost of insurance on the number of 

cars that households choose to own. 

75 Although it is not obvious which change would be larger, there is strong intuition that suggests people 

with higher scores are relatively less likely to be driving without insurance even when scores are not used 

to determine premiums. This would be true if scores were correlated with wealth or if scores were a 

measure of caution or responsibility. The value of liability insurance to an individual depends in part on 

that individual’s wealth, because people with very little wealth may be nearly “judgment proof,” and 

therefore face very little effective risk from liability claims. A company that issues a policy, however, is 

liable up to the policy limits. So, liability insurance may be worth less to a low-wealth driver than it costs, 

(continued) 
41

The FTC sought to estimate the impact of credit-based insurance scores on the 

prevalence of consumers driving without insurance. It is difficult to obtain reliable data 

concerning the number or share of drivers who drive without insurance, because this 

conduct is illegal in most states. In an effort to derive such an estimate, the FTC 

compared the number of uninsured motorist claims relative to other claims filed during 

1996 to 2003 (i.e., when credit-based insurance scores were becoming more widely used) 

for states in which these scores were used and in states in which they were not. 

We assessed how often consumers filed uninsured motorist claims relative to how 

often they filed bodily injury claims and property damage liability claims. Figure 6 

shows that the number of uninsured motorist claims filed compared to the number of 

bodily injury claims filed increased in states where credit-based insurance scores were 

allowed, but decreased slightly in states where they were not.76 Figure 6 also shows the 

number of uninsured motorist claims filed compared to the number of property damage 

claims filed was basically unchanged in states where scores were allowed and decreased 

somewhat in states where they were not.

These results are consistent with the hypothesis that scores, because they raise the 

premiums of some consumers, cause a larger share of consumers to drive without 

insurance 77 and/or more risky consumers to drive without insurance.78 These results, 

 because – if uninsured – the driver would have to pay out less to cover others’ losses from an accident than 

would the insurance company if the drivers bought insurance. 

76 The states identified as not allowing the use of scores during the relevant period of time are California, 

New Jersey, Massachusetts, and Hawaii. Because of limitations in the data, Texas and South Carolina are 

not included in either group. 

77 If reduced cross-subsidization leads to more consumers driving without insurance, this could actually 

lead to lower overall losses from accidents. Research shows that the effect on accidents of requiring 

drivers to buy liability insurance in order to operate a car legally is unclear. Some drivers may choose not 

to purchase insurance and then either not drive or drive less often or more carefully, to avoid detection, 

leading to fewer accidents. Other drivers may purchase insurance they otherwise would have foregone, and 

then drive more often or more riskily, because they no longer bear the liability risk of causing an accident, 

(continued) 
42

however, should be treated with caution. First, the relative change between the groups of 

states took place during the period 1997 – 2000. While scores were becoming widely 

used during this period, credit-based insurance scoring had probably not yet affected 

most consumers’ premiums, given that insurance companies generally do not use scores 

when renewing customers. Perhaps more importantly, the FTC’s analysis could be 

affected by any state-specific changes in insurance markets. Because the number of 

states not allowing the use of credit-based insurance scores for automobile insurance was 

small (California, Hawaii, Massachusetts, and New Jersey), any such changes could 

render them unreliable as a comparison group. In addition, because the analysis relies on 

uninsured motorist claims to indirectly measure the level of driving without insurance, 

differences over time in which consumers carried uninsured motorist coverage in states 

which allow the use of scores and those that do not could affect the results. 

3. Adverse Selection 

Credit-based insurance scores also may make insurance markets more efficient if 

they decrease the extent to which consumers make insurance purchasing decisions using 

better risk information than that available to insurance companies. In a competitive 

market, insurers will offer prices to groups of consumers reflecting the average expected 

risk of loss for each group. But if a consumer has better information than the insurance 

 leading to more accidents. One study has reported that the latter effect predominates over the former effect. 

Alma Cohen and Rajeev Dehejia, The Effect of Automobile Insurance and Accident Liability Laws on 

Traffic Fatalities, 47 J. OF LAW AND ECON. 357 (Oct. 2004). 78 If these results do reflect effects of credit-based insurance scores, they could have the indirect effect of 

mitigating some of the savings that higher-score drivers get from the use of scores. If more higher-risk 

drivers are uninsured, this could increase the expected cost of uninsured motorist claims that insured 

drivers submit under their policies. In turn, this could increase the premiums that insurance companies 

must charge lower-risk consumers to cover these increased uninsured driver claims. Accordingly, even 

these increases in the cost of uninsured motorist coverage may offset somewhat the decrease in premiums 

that higher-score consumers receive from the use of scores. 
43

company about his or her own true expected risk of loss, he or she will know whether the 

group price is higher or lower than his or her true expected claims cost. The consumer 

may use this superior knowledge to determine whether and how much insurance to 

purchase, a phenomenon known as “adverse selection.”79

Adverse selection may be occurring if higher-risk consumers are more likely to 

have insurance or more complete coverage than lower-risk consumers. A higher-risk 

consumer who realizes that he or she is being charged a price that is lower than his or her 

actual risk of loss cost will have an incentive to purchase more insurance coverage.80 A 

lower-risk consumer who realizes that he or she is being charged a price higher than his 

or her actual risk of loss will have an incentive to purchase less insurance coverage.81 If 

higher-risk consumers purchase more insurance coverage and lower-risk consumers 

purchase less insurance coverage, the average risk of the group of consumers who do buy 

insurance will be higher. Premiums then would have to increase for insurance companies 

to cover the total claims costs, providing a further disincentive for lower-risk consumers 

to purchase insurance. If consumers know more about the risk of loss they pose than 

 

79 The discussion here is of market-wide adverse selection, where consumers know more about their risk 

than any firm does. A firm competing in an insurance market can face another form of adverse selection if 

one of its competitors is able to do a better job of predicting risk and entices away low-risk customers while 

leaving behind high-risk customers. See State Farm Comment, supra note 66, at 8. 80 It is conventional wisdom in the insurance industry, however, that the riskiest drivers are those who 

choose to buy the least amount of coverage possible, and would buy no insurance if it were not legally 

required. This conventional wisdom probably reflects, at least in part, that the “riskiest drivers” in question 

are riskiest based on characteristics that are used to underwrite and rate policies, like driving history, and 

they are charged the highest rates. If these drivers really are very risky, simple theory would predict that 

they would still be willing to pay very high rates. Explanations for why these drivers would be unwilling to 

buy insurance at rates that reflect their true risks could include: that the drivers have very limited assets and 

are therefore “judgment proof,” and therefore face less actual risk than the firm would face; that these 

drivers are less risk-averse than other drivers, or even risk-loving, and therefore unwilling to buy insurance 

at market rates; that the drivers believe themselves to be less risky than firms judge them to be; or that the 

drivers are cash-constrained, and do not buy insurance even though they would rather have the insurance 

than face the risk of a large loss. 

81 A consumer may do this by purchasing a policy with large deductibles or low liability limits, by not 

purchasing certain types of coverage, or by not purchasing insurance at all. 
44

insurance companies, it therefore can affect insurance purchasing decisions in ways that 

cause economic inefficiency.82

If scores allow insurance companies to predict risk more accurately, it could 

decrease the difference between what consumers and insurance companies know about 

the risk that individual consumers pose. Insurance companies therefore would be able to 

charge consumers premiums that more accurately reflect the true risk. This would reduce 

the incentive of higher-risk consumers to purchase more insurance and lower-risk 

consumers to purchase less. Accordingly, scores may reduce the extent of adverse 

selection and make insurance markets more efficient.83

The FTC considered whether adverse selection exists in automobile insurance 

markets in the United States.84 It seems unlikely that consumers have better information 

about the risk they pose than do insurance companies. Although consumers might have 

some sense of how much risk they pose based on their own experience, it seems unlikely 

that this sense is more accurate than the assessment insurance companies can make. 

 

82 Insurers who realize that adverse coverage selection is occurring may attempt to separate the higherfrom the lower-risk consumers by offering different price-coverage combinations. One theoretical analysis 

suggests that under some conditions, this approach can reduce the inefficiency caused by adverse selection. 

Michael Rothschild and Joseph Stiglitz, Equilibrium in Competitive Insurance Markets: An Essay on the 

Economics of Imperfect Information, 90 THE Q. J. OF ECON. 629 (Nov. 1976). In fact, firms do offer 

different deductible choices, which could be a mechanism for separating high-risk and low-risk customers. 

It may also be a pricing response by firms to differing levels of risk-aversion among customers. 

83 The American Insurance Association has stated that an insurance company found that, after introducing 

the use of credit-based insurance scores, “there is some evidence that higher limits of liability coverage and

lower physical damage deductibles are being purchased...” AIA Comment, supra note 66, at 14 (emphasis 

in original). This would be consistent with a reduction in adverse selection resulting from the use of scores. 84 Empirical studies have found only limited evidence for adverse selection in automobile markets in other 

countries, and even then only in very special circumstances. See Alma Cohen, Asymmetrical Information 

and Learning: Evidence from the Automobile Insurance Market, 87 REV. OF ECON. AND STATS. 197 (May 

2005) (finding evidence that lower risk drivers in Israel purchased less insurance coverage than higher risk 

drivers, but only for experienced drivers for a limited period of time after switching policies and in a 

country in which insurance companies do not share data on prior claims); Pierre-Andre Chiappori, et al., 

Asymmetrical Information in Insurance: General Testable Implications, (Feb. 24, 2004) (finding no 

evidence that lower risk drivers in France bought less insurance than higher risk drivers), available at 

http://www.iue.it/FinConsEU/papers2004/salanie.pdf. 
45

Specifically, companies make their assessment also using information about the 

consumer’s past experience, such as extensive prior claim information included in a 

database that insurance companies share and public record information, such as 

convictions for driving while intoxicated or speeding. 

Moreover, even assuming that consumers have better knowledge than insurance 

companies of the risk they pose, there are significant limitations on the extent to which 

consumers can use this advantage to alter their insurance purchasing decisions. Most 

states mandate minimum liability coverage for cars, and lenders typically require even 

greater coverage on cars they finance. Even though consumers retain some ability to 

make choices concerning insurance coverage, such as deductibles and limits, these 

choices are limited considerably.85

The FTC analyzed its automobile insurance database to test whether there was 

any indication that adverse selection may be occurring. We found that lower-risk drivers 

tend to have policies with higher deductibles than do higher-risk drivers, that is, lowerrisk drivers have less insurance coverage than higher-risk drivers. This is consistent with 

(but does not prove) adverse selection is occurring in automobile insurance markets. 86 

If credit-based insurance score information is considered in the analysis, i.e., the 

risk information available to insurance companies relative to consumers is enhanced, 

then adverse selection would be expected to decrease. However, when the FTC 

considered scores in its analysis, lower-risk drivers were still found to have insurance 

 

85 It is clear that adverse selection experienced by a single firm can be a powerful force. When different 

firms have significantly different risk prediction technology, consumers will see the different prices 

charged and will tend to choose the firms with lower premiums. This can lead to a negative-feedback loop 

that can even cause a firm to collapse. 

86 One alternative explanation is moral hazard. If people with more complete coverage take less care, 

because they bear less of the cost of any accident or other damage or loss, this would result in the same 

relationship. 
46

policies with higher deductibles than higher-risk drivers. This suggests that adverse 

selection may not be occurring, or, if it is occurring, then scores may not reduce it. 

 

B. Other Possible Effects of Credit-Based Insurance Scores

Innovations in risk prediction techniques like credit-based insurance scores may 

affect the availability of insurance and some of the costs associated with selling 

insurance. First, some consumers may have a broader range of options to choose from 

when purchasing insurance. Because credit-based insurance scores predict risk more 

accurately for consumers, insurance companies now may be willing to offer coverage to 

some higher-risk consumers. In addition, credit-based insurance scores may make the 

process of underwriting and rating quicker and cheaper, and competition between 

insurance companies may cause cost savings from these process improvements to be 

passed on to consumers in the form of lower premiums. 

Insurance companies and industry representatives stated that the use of creditbased insurance scores gives firms greater confidence in their ability to predict the risk 

that consumers pose. That is, if firms have more confidence in their risk estimates, they 

may be able to offer insurance to customers for whom they would otherwise not be able 

to determine an appropriate premium. The American Insurance Association, for 

example, has stated “(m)ore precise pricing enables insurers to accept greater risk by 

ensuring that both good risks and more marginal risks are properly priced to reflect the 

exposure they represent.”87 Several firms, including The Hartford and MetLife Home and 

Auto, have stated that the use of credit-based risk scores enabled them to offer policies to 

 

87 AIA Comment, supra note 66, at 4. 
47

higher-risk consumers than they had previously.88 This could lead to higher-risk 

consumers having more choices as they shop for insurance. No data, however, were 

submitted or obtained to assess the extent to which credit-based insurance scores actually 

have expanded insurance choices for higher-risk consumers. 

In addition, several insurance companies and score developers emphasized that 

the use of scores can save costs. Specifically, they asserted that the use of scores 

facilitates automation, speeds up policy underwriting and rating, and otherwise reduces 

the costs of underwriting and rating.89 No data was submitted or obtained to allow the 

FTC to develop reliable estimates of cost-savings associated with credit-based insurance 

scores. Assuming that there are such savings, the FTC would anticipate that competition 

in the market for automobile insurance would result in these savings being passed on to 

consumers in the form of lower prices. 

Further, banning the use of factors that are known to be correlated with risk could 

have negative effects on insurance markets. If firms cannot adjust prices based on the 

risk associated with a characteristic, they will have an incentive to refuse to offer policies 

to people with the characteristic.90 If the law prohibits firms from refusing to sell policies 

to people with that characteristic, they will still have an incentive to try to avoid insuring 

them. This could cause firms to expend resources on finding ways to avoid higher-risk 

consumers, reducing the availability of insurance to higher-risk consumers and making 

otherwise profitable distribution channels untenable. 

 

88 Meeting between FTC staff and The Hartford (July 14, 2004); Meeting between FTC staff and MetLife 

Home and Auto (July 12, 2004); Meeting between FTC staff and USAA, (July 14, 2004). See also AIA 

Comment, supra note 66, at 7-8; NAMIC Comment, supra note 20, at 6-7. 89 AIA Comment, supra note 66, at 12; Fair Isaac Comment, supra note 18, at 15; State Farm Comment, 

supra note 66, at 4. 90 Id. at 10. 
48

A simple example illustrates the possible impact of banning the use of a 

characteristic in making the decision whether to offer insurance to consumers. Assume 

that geographic location is correlated with risk on insurance policies91 and that firms are 

allowed to refuse to sell insurance based on geography but are not allowed to charge 

different prices based on geography. This would give insurance companies an incentive 

to refuse to sell policies to people living in riskier areas.92 If firms could not outright 

refuse to sell policies based on geography, and could not charge different prices based on 

geography, they would have an incentive to use other means to avoid insuring those who 

live in more risky geographic areas, for example, not establishing offices, working with 

independent agents, or advertising in these locations. 

It is not clear, however, whether banning the use of credit-based insurance scores 

would lead to distortions of the insurance market like those associated with banning the 

use of geography. An insurance company does not see a consumer’s score until he or she 

applies for insurance coverage. It therefore would be difficult for insurance companies to 

directly avoid selling insurance to consumers with low scores. There may be, however, 

different marketing approaches, such as alternative types of advertising, which bring in 

consumers with different average scores. If firms cannot use scores to underwrite or rate, 

they would have an incentive to market only through channels that bring in consumers 

with higher scores. This could reduce the availability of information about insurance 

options, particularly to consumers with lower insurance scores. No data was submitted or 

obtained, however, to permit the FTC to determine whether restrictions on the use of 

scores actually would have this type of effect. 

 

91 Id. 92 Firms might specialize geographically, with firms with higher premiums offering policies everywhere but 

mainly getting customers from high risk areas, while lower-cost firms refuse to write in high-risk areas. 
49

Banning credit-based insurance scores may also give firms greater incentives to 

invest in developing other risk-prediction tools. If the use of scores is banned, firms may 

have an incentive to spend more on developing new risk variables to capture some of the 

same risk prediction benefits of scores.93 This could be seen as an unnecessary societal 

cost, given that scoring technology has already been developed and scores are a fairly 

low-cost risk prediction technique. 

 C. Effects on Residual Markets for Automobile Insurance

The introduction and growth in the use of credit-based insurance scores has taken 

place during a time when one particular measure of the functioning of the market, the 

share of consumers buying insurance through state-run “residual markets,” indicated the 

market was working well. All states run some type of program to allow consumers to 

purchase automobile insurance when they are unable to find a private firm willing to sell 

them policies voluntarily.94 To avoid attracting consumers who could otherwise obtain 

private insurance coverage, these state-run programs typically charge higher prices than 

private insurance companies. 

Figure 7 shows the share of automobile policies that were purchased through 

state-run programs during the years 1996 – 2003, broken down by states that allow the 

use of credit-based insurance scores, and those that do not.95 It shows that a larger share 

of consumers participated in these programs in the states that did not allow the use of 

 

93 See Cheng-Sheng Peter Wu, Deloitte and Touche, What to do When You Cannot Use Credit (Sept. 2000) 

(presentation at the CAS Special Interest Seminar), available at 

http://www.casact.org/education/specsem/f2005/handouts/credit.ppt. 94 See https://www.aipso.com/about.asp. 95 The states identified as not allowing the use of scores during the relevant period of time are California, 

New Jersey, Massachusetts, and Hawaii. Because of limitations in the data, Texas and South Carolina are 

not included in either group. 
50

scores. However, this was true both before and after the introduction of scores, and 

therefore this difference in levels of participation presumably reflects other differences 

between states. Figure 7 shows that the state-run program share fell during the second 

half of the 1990s, as score were being introduced, and then leveled off after 2000. The 

pattern is nearly identical in states that allowed the use of scores and states that did not. 

Therefore, Figure 7 is probably best interpreted as meaning that scores at least did not 

interfere with the smooth functioning of automobile insurance markets. 

VI. EFFECTS OF SCORES ON PROTECTED CLASSES OF CONSUMERS

FACTA requires that the FTC analyze the extent to which the use of credit-based 

insurance scores affects the availability and affordability of insurance for members of 

certain categories of consumers. The statute mandates that the Commission consider the 

impact of these scores on categories of consumers based on race, ethnicity, national 

origin, geography, income, religion, age, sex, and marital status. In particular, the agency 

was instructed to assess whether scores act as a proxy for membership in these groups. 

In fulfilling the statutory mandate, the FTC focused its analysis on the effect of 

credit-based insurance scores on consumers in different racial, ethnic, national origin, and 

income groups. The Commission did not focus its assessment on consumers in different 

religious groups because we are not aware of any reliable data relating scores to religious 

affiliation. In addition, the FTC also did not focus its analysis on consumers in different 

geographic, age, sex, and marital status groups. In most locations in the United States, 

insurance companies can and do use geography, age, sex, and marital status directly in 
51

determining automobile insurance premiums.96 While credit-based insurance scores may 

vary based on these factors, the direct effect of using these factors to price insurance far 

outweighs any indirect effects these factors may have through their impact on scores. 

The FTC therefore did not try to measure any such indirect effects. 

 A. Credit-Based Insurance Scores and Racial, Ethnic, and Income Groups

1. Difference in Scores across Groups 

 The FTC analyzed whether there was a relationship between credit-based 

insurance scores and race, ethnicity, national origin, and income. In undertaking this 

analysis, the Commission first reviewed and considered prior research. In 1999, the 

Virginia State Corporation Commission’s Bureau of Insurance issued a report assessing 

the relationship at the ZIP code level between scores and race as well as between scores 

and income.97 The report stated that “nothing in (our) analysis leads the Bureau to the 

conclusion that income or race alone is a reliable predictor of credit scores.” 

Nevertheless, the absence of more detailed information about the results of this study 

leaves unclear the relationship between scores and race and income. 

The State of Missouri Department of Insurance released a study in 2004 that 

relied on similar data.98 The Missouri study used ZIP-code level data on scores and race, 

income, and other demographic variables. The scores analyzed were credit-based 

insurance scores that twelve large insurance companies used for automobile or 

 

96 While the use of income to underwrite policies or set rates may not be expressly prohibited in some 

locations, it appears to be generally regarded as an illegitimate variable for those purposes. 

97 Report of the State Corp. Comm’n’s Bureau of Ins. to the Sen. Commerce and Labor Comm. of the Gen. 

Assemb. of Va., Use of Credit Reports in Underwriting (1999) [hereinafter Virginia Study]. 98 Brent Kabler, Ph.D., Insurance-Based Credit Scores: Impact on Minority and Low Income Populations 

in Missouri (Jan. 2004) [hereinafter Missouri Study], available at

http://www.insurance.mo.gov/reports/credscore.pdf. 
52

homeowners policies. The Missouri study found that scores were correlated with the 

racial, ethnic, and income characteristics of ZIP codes. Specifically, as the proportion of 

racial and ethnic minorities or lower-income consumers in a ZIP code increased, scores 

decreased.99 These correlations remained after controlling for education, marital status, 

and housing values. 

Unlike prior researchers, the Texas Department of Insurance (TDI) in its 2004 

study moved beyond aggregate data and obtained data about individuals to analyze the 

relationship between scores and race, ethnicity, and income. The TDI used automobile 

and homeowners policy data from six large insurance companies. The TDI obtained race 

data for each consumer from the Texas Department of Public Safety and ethnicity data 

for each consumer from a Hispanic surname match. The TDI used median income for the 

ZIP code in which consumers lived, because individual income information was not 

available. 

The TDI’s analysis of this data showed that African Americans and Hispanics 

tended to have lower scores than Asians and whites.100 It revealed mixed results for 

income. For some insurance companies, consumers in higher-income areas had higher 

scores, while this was not the case for other insurance companies. It is not clear whether 

these different results for income reflect differences in the credit scoring models that the 

insurance companies used, or in the mix of customers at each firm. 

 

99 An attempt was also made in the Missouri study to use the ZIP code level data to draw inferences about 

individual-level differences in credit scores by race and income. The results of this analysis were more 

speculative, but did demonstrate that it would be very unlikely that the differences found at the ZIP code 

level could have been found if there were no differences at the individual level. 

100 The TDI characterized the scores in this way: “In general, Blacks have an average credit score that is 

roughly 10% to 35% worse than the credit scores for Whites. Hispanics have an average credit score that is 

roughly 5% to 25% worse than those for Whites. Asians have average credit scores that are about the same 

or slightly worse than those for Whites.” 2004 Texas Report, supra note 41, at 13. 
53

After reviewing the prior research, the FTC analyzed the information in its own 

automobile insurance database to assess the relationship between scores and race, 

ethnicity, national origin, and income. Figure 8 shows how non-Hispanic whites, 

African Americans, Hispanics, and Asians are distributed across the range of scores. 

The horizontal axis shows score deciles, and the vertical axis shows the share of each 

group that falls in each decile. The deciles are defined using the overall distribution of 

scores. If a group had the same distribution of scores as the overall sample, then 10% of 

that group’s population therefore would fall in each of the ten deciles. 

Figure 8 shows that non-Hispanic whites and Asians are fairly evenly distributed 

across the score range, resulting in a roughly flat line near 10%. In contrast, African 

Americans and Hispanics are strongly over-represented in the lowest deciles and underrepresented in the highest deciles. For example, 26% of African Americans are in the 

group with the lowest 10% of credit-based insurance scores, while only 3% are in the 

highest 10% of scores. Similarly, 19% of Hispanics are in the group with the lowest 10% 

of scores, and 5% are in the highest 10% of scores. 

Another way of measuring these differences is to look at where the median 

person101 for each racial or ethnic group falls in the overall distribution of scores. If 

scores were distributed identically across racial and ethnic groups, the median score for 

each group would equal the overall median – the 50th percentile. The FTC’s data show 

that the median scores for non-Hispanic whites and Asians are quite similar to that of the 

overall sample, with the median score for non-Hispanic whites and Asians falling in the 

54th and 52nd percentile, respectively. In contrast, the median scores for the African 

 

101 One-half of the group will have a score lower than the median person and one-half will have a score 

higher than the median person. 
54

Americans and Hispanics are much lower, with the median scores of African Americans 

and Hispanics falling in the 23rd and 32nd percentile, respectively. So, more than one-half 

of all African Americans have credit scores in the lowest quarter of the overall score 

distribution, and one-half of all Hispanics have credit scores in the lowest third of the 

overall score distribution. 

Figure 9 presents an alternative way of viewing these differences. It shows the 

racial and ethnic makeup of each decile in the score distribution, which varies 

considerably across the range of scores. Because non-Hispanic whites make up such a 

large share of the populations, they are a majority in every score decile. But, as Figure 8 

shows, African Americans and Hispanics are heavily over-represented in the lower score 

deciles. 

In addition to race and ethnicity, the FTC examined the relationship between 

scores and national origin. To assess this relationship, the Commission compared scores 

for foreign-born consumers with those of consumers born in the United States. The 

scores for consumers born outside the United States were slightly lower than those of 

consumers born in the United States, with the median score of the foreign-born 

consumers falling in the 44th percentile of all scores. 

The FTC also compared the scores for recent immigrants and other consumers.102 

Recent immigrants have scores that are slightly lower than other immigrants and lower 

than consumers overall, with the median score for recent immigrants falling in the 39th

percentile of all scores. We found that recent immigrants whose information is included 

 

102 The FTC database does not contain information on the actual date of anyone’s arrival in the United 

States. For this reason, recent immigrants were defined as people who first applied for a Social Security 

card during the previous ten years, and who were 30 years old or older at the time of the sample. These 

restrictions were an attempt to limit “recent immigrants” to people who arrived in the United States as 

adults. 
55

in the FTC database were much more likely to be Hispanic or Asian than consumers born 

in the United States. This makes it complicated to evaluate and describe the relationship 

between scores and race or ethnicity apart from the effect of national origin. Because 

race and ethnicity are associated with much larger differences in scores than national 

origin, the Commission focused its further analysis on race and ethnicity. 

Finally, the FTC study evaluated the relationship between scores and income. 

The Commission did not have access to information about the income of the particular 

consumers in its database. The FTC instead used the median income of the United States 

Census tract in which consumers live to divide them into low-to-moderate income, 

middle income, and high-income neighborhood groups. 103 Figure 10 shows the share of 

people in each income category in each decile of the distribution of scores. Low-tomoderate income consumers are somewhat over-represented in the lower score deciles, 

with 15% of these individuals in the lowest 10% of scores, and only 8% in the highest 

10% of scores. Middle-income consumers are essentially evenly distributed across the 

distribution of scores. High-income consumers are under-represented in the lowest 10% 

of the score distribution, but otherwise fairly evenly distributed. Figure 11 shows the 

income breakdown of each score decile. Again, it shows that there is some relationship 

between neighborhood income and score. 

The results for the FTC’s database show that as income increases, scores tend to 

increase. These results, however, are much weaker than the results for race and ethnicity. 

 

103 This approach follows methods used to analyze income in FRB studies of mortgage markets. The 

groups were: (1) Low-to-moderate income: Tract median < 80% of MSA median income; (2) Middle 

income: Tract median >= 80% of MSA median income and <%120 of MSA median income; and (3) High 

income: Tract median >= %120 of MSA median income. As discussed in Appendix F, we have also done 

much of the analysis using absolute median income, instead of income relative to the MSA, and the results 

are not qualitatively different. 
56

This may be because the relationship between score and income actually is weaker, or it 

may simply be the result of only having data on income at the neighborhood level. 

 

 2. Possible Reasons for Differences in Scores across Groups

As discussed above, the FTC’s analysis shows a relationship between credit-based 

insurance scores and race, ethnicity, and, to a lesser extent, income. The Commission 

examined other information in its sample to determine what factors could account for 

differences in scores among racial and ethnic groups. The FTC’s database contains some 

information on factors that could explain some of the differences in scores among racial 

and ethnic groups. Specifically, it includes information on the median income of the 

neighborhood in which each consumer lives, and consumers who live in lower-income 

neighborhoods tend to have lower scores. It also contains information from which the 

age of the consumers whose score is in the database can be inferred,104 and older 

consumers tend to have higher scores. Finally, the FTC’s database contains information 

about the gender of the consumers whose score is included (the “first named insured” on 

the policy), and men in the FTC database tend to have higher scores than women, 

although the difference in average score between men and women in the FTC database 

cannot be generalized to the overall population.105

 

104 For single-driver households, we know the age of the person for whom we have a credit score. For 

multi-driver households, we need to make an assumption about whose age we have. We do this in several 

ways. From Social Security Administration (“SSA”) data, we know the gender of the person whose credit 

score we have. If there is only one driver in a household with that same gender, we assume that person is 

the person for whom we have a credit score. If there are multiple people whose gender matches the SSA 

data, we take the oldest, on the assumption that that person is most likely to be the first named insured. 

105 We have a score for only one person covered by each policy. From examining our data, it is apparent 

that in households with male and female adults (e.g., married couples), it is most often the male driver who 

is the first named insured, and therefore the person for whom we have a score. About 75% of multi-driver 

policies have a male first named insured, while the split for single-driver policies is 50/50. So, it appears 

that the men for whom we have scores are much more likely to be married than the women for whom we 

(continued) 
57

Table 3 presents median neighborhood income, age, and gender for racial and 

ethnic groups for consumers whose information is in the FTC database. It shows that 

African Americans and Hispanics live in neighborhoods with lower median incomes than 

non-Hispanic whites and Asians. It reveals that Hispanics and Asians are younger than 

non-Hispanic whites and African Americans. It further shows that the African-American 

customers in this sample are much more likely to be female than are customers in other 

racial and ethnic groups.106 All of these differences are consistent with African 

Americans and Hispanics having lower credit scores. 

Figure 12 shows the distribution of scores by race and ethnicity after controlling 

for neighborhood income, age, and gender of the person scored. It shows that large 

differences remain in the distributions of scores across racial and ethnic groups, and that 

these differences are only slightly smaller than they were prior to controlling for these 

factors.107 In particular, prior to controlling for these factors, the median score for 

African Americans and Hispanics was in the 23rd and 32nd percentiles, respectively. 

After using these controls, the median score for African Americans and Hispanics rose to 

the 27th and 37th percentiles, respectively. In short, consideration of neighborhood 

income, age, and gender explains only a small part of the difference in credit-based 

insurance scores between racial and ethnic groups. It is not clear what explains the rest 

of the difference. 

 have scores. The differences in score by gender in the FTC database, therefore, cannot be interpreted as the 

difference in scores that would be observed between all men and all women, because they also reflect 

differences in credit score by marital status and household size. 

106 Recall that age and gender, like score, are for the customer who was the “first named insured” on a 

policy. 

107 It is our understanding that the Federal Reserve Board is undertaking a similar analysis using a richer set 

of data about each individual. 
58

3. Impact of Differences in Scores on Premiums Paid

a. Effect on Those for Whom Scores Were Available

The FTC assessed the implications of the differences in credit-based insurance 

scores for the premiums that members of different racial and ethnic groups would be 

predicted to pay. As discussed above, the FTC database can be used to predict 

differences in claims risk with and without the use of scores. These differences, in turn, 

can be used to estimate the effects of scores on expected insurance premiums for racial, 

ethnic, and income groups. 

Figure 13 shows the results of the FTC’s analysis. These are graphs that show the 

share of each group with different size changes in their predicted risk between models 

where scores were not used and models where scores were used. Comparing across 

groups clearly shows that a much larger share of African American and Hispanics had 

increases in their predicted risk than did non-Hispanic whites and Asians. When scores 

are used, the predicted risk decreased for 62% of non-Hispanic whites and 66% of 

Asians. On the other hand, the predicted risk increased for 64% of African Americans 

and 53% of Hispanics. These results flow from the fact that, as discussed above, the 

scores for African Americans and Hispanics are lower on average than the scores of nonHispanic whites and Asians. 

Table 4 shows the magnitude of changes in predicted risk for racial and ethnic 

groups as a result of the use of scores. The average predicted risk increased by 10% for 
59

African Americans and 4.2% for Hispanics, and dropped by 1.6% for non-Hispanic 

whites and 4.9% for Asians.108

b. Effect on Those for Whom Scores Were Not Available

The FTC also sought to determine whether the likelihood that a credit-based 

insurance score could not be generated for a consumer varied across racial and ethnic 

groups, and what impact any such differences would be expected to have on the 

premiums paid by consumers. A score may not be available for a consumer for one of 

two reasons: either it cannot be located for a consumer (a “no-hit”), or a consumer may 

have a credit history file, but it may not contain information sufficient to calculate a 

credit-based insurance score (a “thin file”). 

The FTC database does not contain Social Security Administration race and 

ethnicity data for most customers who were “no hits” or “thin files.”109 The FTC 

therefore used United States Census data to determine whether there are differences in 

the proportions of racial and ethnic groups that do not have a credit score. Based on 

block-level data, the Commission estimates that credit reports could not be located for 

9.7% of African Americans, 9.2% of Hispanics, 7.8% of non-Hispanic whites, and 6.4% 

of Asians. Similarly, 2.4% of Hispanics, 2.1% of African Americans, 1.8% of non-

 

108 The relatively large decrease in predicted risk for Asians relative to non-Hispanic whites was surprising, 

given how similar the score distributions are for these two groups. In addition, the increase in predicted 

risk for Hispanics was only half that of African Americans, even though Hispanics have average scores 

closer to African American than to the overall population. Further examination of the results of the models 

showed that the inclusion of scores affected the impact of other variables on predicted risk. This, in turn, 

affected the predicted risk of Asians and Hispanics. In particular, the impact that short tenure with a firm 

and low liability limits had on predicted risk shrank considerably when scores were included in the models. 

Asians and Hispanics have low average tenure and low average liability limits, so when the impact of those 

characteristics on predicted risk decreased, so did the average predicted risk of Asians and Hispanics. 

109 The process of obtaining SSA race and ethnicity data relied on obtaining Social Security Numbers or 

dates of birth from credit reports; thus we did not receive SSA information for people whose credit reports 

could not be located, or who had very little information in their reports. Similarly, we do not have SSA 

national origin information for these people, and therefore cannot analyze the impact on immigrants of a 

lack of a credit-based insurance score. 
60

Hispanic whites, and 1.8% of Asians had credit reports, but with too little information 

available to calculate a score.110 Note that because these results are based on geographic 

data, they may not exactly reflect actual differences between racial and ethnic groups. 

The FTC’s assessment indicates that consumers for whom scores were not 

available appeared slightly riskier when scores were considered than when they were not. 

The Commission compared the results from risk models without scores with results from 

risk models with scores that also included categories for “no hit” and “thin file” in 

making this determination. No-hit consumers were 1.06 times riskier in a model that 

included controls for scores compared to a model that did not. Thin-file consumers were 

1.02 times riskier in a model that included controls for scores compared to a model that 

did not. 

Given the relatively small differences across groups in the share of people who 

were “no hits” or “thin files,” and the relatively small impact of not having a score on 

predicted risk (as opposed to the large impacts of using scores on the predicted risk of 

people in the lowest score deciles, for example), this is unlikely to be an important source 

of differences in premiums across racial and ethnic groups. Again, this analysis is limited 

by the lack of individual-level data on race and ethnicity for people for whom we do not 

have credit scores. 

 

110 The block data were used by assuming each person had a likelihood of being a member of each racial or 

ethnic group that was proportional to the share of the population of each group in that person’s block. This 

is implemented similarly to how imputed race/ethnicity information for SSA data are used. See Appendix 

C for a discussion of that process. 
61

B. Scores as a Proxy for Race and Ethnicity

Section 215 of FACTA mandates that the FTC create a statistical model of 

insurance claims that includes credit-based insurance scores, standard non-credit risk 

variables, and controls for protected classes under the Equal Credit Opportunity Act.111 

We understand this to require the agency to analyze whether credit-based insurance 

scores act as a “proxy” for membership in these classes. As discussed above, we focused 

our analysis on effects on different groups defined by race, ethnicity, and income. 

Understanding how a proxy functions is critical to the FTC’s analysis. Insurance 

companies build statistical models that relate a variety of characteristics of customers 

(e.g., age or driving history) to risk. Firms then use these models to predict the average 

claims that customers with those characteristics will generate, and these predictions of 

risk play a central role in determining the premiums that firms charge. 

 The risk models that companies build do not include information about race, 

ethnicity, or income. If there are large differences in average risk based on race, 

ethnicity, or income, then models may attribute some of those differences in risk to other 

variables included in the model that differ across these groups. The included variable 

thus may act in whole or in part as a statistical “proxy” for the excluded variables of race, 

ethnicity, or income.112

The FTC sought to determine whether credit-based insurance scores act as a 

proxy for race, ethnicity, and income in insurance decisions. To determine whether there 

is such an effect, and, if so, its magnitude, the Commission conducted three related 

 

111 FACTA § 215(a)(2) (2006); 15 U.S.C. §1681 note (2006). 112 The econometric term for this effect is “omitted variable bias.” The omission of a predictive variable 

(such as race, ethnicity, or income) causes the estimated effect of a variable that is correlated with the 

omitted variables, such as score, to be “biased” away from the true effect. In this scenario, the direction of 

the bias would be to overstate the relationship between score and claims. 
62

analyses. First, the Commission analyzed whether scores predict risk within racial, 

ethnic, and income groups. If scores do not predict risk within any group defined by 

race, ethnicity, and income, then the sole reason that scores predict risk in the general 

population would be because they act as a proxy for membership in different groups. 

Second, the Commission analyzed whether average risk differed substantially by 

race, ethnicity, and income. If there were no substantial differences in the average risk 

across racial and ethnic groups, then there would be no underlying difference for which 

scores could act as a proxy. If there are substantial differences in risk across groups, 

scores may in part act as a proxy, even if scores also predict risk within groups (and are 

therefore not solely acting as a proxy for membership in different groups). 

 Third, the FTC created models that included controls for race, ethnicity, and 

income, along with credit-based insurance scores and the full range of other predictive 

variables. The Commission quantified the proxy effect of scores by measuring the 

impact of including these additional controls on the estimated relationship between scores 

and claims. To provide a basis for comparison, the FTC also conducted this analysis for 

several other variables that are predictive of risk. 

1. Do Scores Act Solely as a Proxy for Race, Ethnicity, or Income?

Whether credit-based insurance scores predict risk within racial, ethnic, and 

income groups provides critical insight into whether scores are a proxy for membership 

in these groups. If scores did not predict claims within racial, ethnic, and income groups, 

the relationship between scores and claims must come from scores acting as a proxy for 

race, ethnicity, and income. On the other hand, if scores do predict risk within groups, 

then they do not serve solely as a proxy if used to assess risk for all consumers. 
63

Therefore, the FTC analyzed whether scores predict risk within race, ethnicity, and 

income groups. 

The results of the FTC’s analysis are presented in Figure 14 for each racial and 

ethnic group for each type of automobile insurance coverage. If credit-based insurance 

scores predict the amount that insurance companies paid out in claims within each group, 

there should be a downward slope on each graph.113 In other words, as scores increase for 

members of each group, the amount paid out on claims should be decreasing. 

Although the relatively small sample size for the minority groups in the FTC 

database (which is a particular problem for bodily injury coverage, which has relatively 

few claims) leads to results that sometimes vary substantially from decile to decile, the 

overall pattern observed is that the amount paid out decreases as credit-based insurance 

scores increase for each group for each type of coverage.114 With the exception of 

collision coverage, very few of the decile and coverage combinations have estimated risk 

for a given racial or ethnic group that is statistically significantly different from that of 

the overall sample.115 Because they show that scores predict risk within groups, these 

results show that credit-based insurance scores do not predict risk solely by acting as a 

 

113 These were estimated by including interaction terms between the race/ethnicity variable and the scores 

variables. The coefficients on non-race/ethnicity non-score variables are therefore forced to be the same 

across groups. Entirely separate models cannot be estimated for many race/ethnicity/coverage 

combinations, because the small sample size of the minority groups often leads to the non-convergence of 

the estimation procedure. 

114 One cell that jumps out as being out of line with that pattern is the ninth decile for African Americans 

for comprehensive coverage. Further investigation showed that this result was affected by an outlier; a 

single individual with a very large claim, very low earned car years, and a very large nationallyrepresentative weight had a large impact on the estimated risk for this decile. There are also few African 

Americans in the ninth decile. The difference between the estimated risk for African Americans in the 

ninth decile and the overall sample in the ninth decile was not statistically significant. When this outlier 

was dropped the risk estimate for this decile was similar to the surrounding deciles. The treatment of 

outliers is discussed in Appendix F. 

115 Statistical significance was determined using a bootstrap procedure with 500 replications. The bootstrap 

procedure is discussed in Appendix D. 
64

proxy for membership in racial and ethnic groups.116 

The FTC conducted the same analysis based on neighborhood income. These 

results are shown in Figure 15. These graphs show a consistent negative relationship 

between amount paid and credit-based insurance score for all neighborhood income 

groups. In other words, as scores increased, claims decreased for all income groups. 

In short, because scores do predict risk within racial, ethnic, and income groups, 

they do not act solely as a proxy for these characteristics. 

2. Differences in Average Risk by Race, Ethnicity, and Income

Even though scores do not act solely as a proxy for race, ethnicity, and income, 

there may still be some proxy effect. For such a partial proxy effect to occur, there must 

be differences in average risk among racial, ethnic, or income groups, i.e., scores can 

only have a proxy effect if there is an underlying relationship for which scores can serve 

as a proxy. To determine whether such differences exist, the FTC created models that 

evaluated the relative amount paid on claims by race, ethnicity, and neighborhood income 

for the four main types of automobile insurance coverage. These models included other 

risk variables, but not scores. The results of this analysis are shown in Table 5. For 

purposes of comparisons in these tables, the FTC assigned a relative value of 1 to the 

amount of claims that would be expected to be paid to non-Hispanic white consumers and 

to consumers living in high income neighborhoods. 

Column (a) shows that Asians and Hispanics had a higher amount of claims paid 

under property damage liability coverage than did African Americans and non-Hispanic 

 

116 We also did the same analysis for “foreign born” and “recent immigrants.” The results were similar, and 

scores are correlated with risk for those groups. 
65

whites, although the difference was not statistically significant for Hispanics. It also 

shows that there was very little relationship between the amount of property damage 

liability claims and whether a consumer lives in a neighborhood with a low, middle, or 

high income. While Asians did have more claims under property damage liability 

coverage, as discussed above, our analysis showed that they had scores that were similar 

to the scores of the overall distribution. Therefore, scores cannot act as proxy for being 

Asian, so it is unlikely that scores could act as a proxy for race or ethnicity in a model of 

property damage liability claims. 

Columns (b) through (d) of Table 5 present results concerning the amount paid 

out for bodily injury, collision, and comprehensive coverage, respectively. After 

controlling for other risk factors, insurance companies paid out 48% more to African 

Americans than non-Hispanic whites for bodily injury, 43% more for collision, and 63% 

more for comprehensive coverage. Similarly, they paid out 25% more to Hispanics than 

non-Hispanic whites for bodily injury, 33% more for collision, and 45% more for 

comprehensive coverage. Insurance companies paid out 30% more to Asians than nonHispanic whites for collision coverage. These differences were all statistically 

significant. The differences for bodily injury liability and comprehensive coverages 

between Asians and non-Hispanic whites were relatively small and not statistically 

significant. The large differences in average risk on comprehensive coverage for 

Hispanics and African Americans should be treated with some caution, as the geographic 

risk variable in the FTC database is not a very effective control for geographic variation 
66

in risk on comprehensive coverage.117 

Table 5 shows that the differences among neighborhood income groups were 

much smaller than those among racial and ethnic groups. The one substantial difference 

in risk was that customers in low-income neighborhoods pose a 16% higher risk for 

comprehensive coverage. Again, this may in part be due to the lack of an effective 

geographic risk measure for comprehensive coverage.118

These results show that there were substantial differences in the average risk of 

consumers in different racial and ethnic groups for all four major automobile insurance 

coverages.119 For property damage liability coverage, Asians were the only group with 

 

117 The geographic risk measure in the FTC database is based on property damage liability claims, which 

result from accidents. The estimated effect of the geographic risk measure is much smaller in the 

comprehensive coverage risk models than in the models for the other coverages, suggesting that it is a poor 

control for geographic variation in comprehensive coverage risk. According to the Bureau of Justice 

Statistics, African Americans and Hispanics are much more likely to be victims of automobile theft (a risk 

covered by comprehensive coverage) than non-Hispanic whites. See Bureau of Justice Statistics file 

cv0516.csv, available at www.ojp.usdoj.gov/bjs/pub/sheets/cvus/2005/cv0516.csv; Bureau of Justice 

Statistics file cv0517.csv, available at www.ojp.usdoj.gov/bjs/pub/sheets/cvus/2005/cv0517.csv. In the 

absence of a good measure of the geographic variation in comprehensive coverage risk, race, ethnicity, and 

neighborhood income are likely picking up some of that variation in risk (e.g., they may be acting as a 

proxy for other characteristics of neighborhoods that affect comprehensive coverage risk). Additional 

support for this hypothesis was found by estimating separate risk and severity models that included race, 

ethnicity, and income controls. In those models, race, ethnicity, and income affected only frequency in the 

property damage liability, bodily injury liability, and collision coverage models. In the comprehensive 

coverage model, race, ethnicity, and income were strongly related to claim severity. This is consistent with 

those variables being related to the likelihood of theft claims. 

118 Id. 119 We found similar patterns when we used loss ratios as the measure of relative risk, instead of the direct 

results of the risk models. The loss ratio is the ratio of payments companies made on claims divided by 

premiums customers paid in. Using loss ratios, therefore, shows whether customers in different racial and 

ethnic groups generated greater or lesser total payouts on claims, on average, than predicted by the 

companies, as reflected in the premiums the customers were charged. Loss ratios were fairly similar across 

groups for property damage liability coverage, with Hispanics and Asians generating somewhat more 

claims relative to premiums than African Americans and non-Hispanic whites. For bodily injury liability 

coverage, collision coverage, and comprehensive coverage, African Americans and Hispanics generated 

higher claims relative to premiums than did non-Hispanic whites. The same was true for Asians for 

collision coverage, although Asians had a substantially smaller loss ratio for comprehensive coverage than 

did any other group. For example, the loss ratios of African Americans and Hispanics for collision 

coverage were 83.9% and 85.6%, respectively, for Asians 78.2%, and for non-Hispanic whites the loss ratio 

was 63.3%. Unlike in our risk models, the coverage with the largest differences across groups was bodily 

injury liability coverage, as opposed to comprehensive coverage. This again suggests that part of the 

reason we find such large differences in risk across groups for comprehensive coverage in our models is the 

lack of a geographic risk measure that is specific to risk on comprehensive coverage. For the four 

(continued) 
67

significantly higher risk. For the other three coverages, Hispanics and African Americans 

had substantially higher average payouts on claims than did non-Hispanic whites. Given 

that Hispanics and African Americans have much lower credit-based insurance scores, on 

average, than do non-Hispanic whites, there is the potential that scores could gain 

additional predictive power by acting as a proxy for race and ethnicity in models of 

claims under bodily injury, collision, and comprehensive coverages. 

3. Controlling for Race, Ethnicity, and Income to Test for a Proxy Effect 

a. Existence of a Proxy Effect

The FTC created models that evaluated the relative amount paid on claims by 

score decile with and without controls for membership in racial, ethnic, and income 

groups for the four main types of automobile insurance coverage. Table 6 shows the 

results.120 For purposes of comparisons on this Table, the FTC assigned the relative value 

of 1 to: (1) the amount of claims that would be expected to be paid to consumers in the 

highest 10% of credit-based insurance scores; (2) non-Hispanic white consumers; and (3) 

consumers living in high-income Census tracts. For each coverage, the first column 

shows the predicted relative amount of claims for credit-based insurance score deciles for 

a model that does not include controls for race, ethnicity and income. The second 

column for each coverage shows the results from models that include scores and controls 

for the prohibited factors. 

Comparing the two columns for property damage liability coverage (columns (a) 

and (b)) reveals that there was very little difference in the impact of credit-based 

 coverages combined, the loss ratios of the four groups were: for non-Hispanic whites, 62%; for African 

Americans, 80%; for Hispanics, 81%; and, for Asians, 67%. 

120 Again, the models used here are Tweedie GLMs. Modeling details are given in Appendix D. 
68

insurance scores on predicted risk based on whether the model included controls for 

membership in a protected class. The only statistically significant difference was that the 

estimated relative risk for the lowest score decile was larger when protected class 

controls were included in the model. 121 This is opposite of the change that would occur if 

scores were acting as a proxy. This lack of a proxy effect is not surprising, given that the 

only statistically significant difference in risk by racial or ethnic group for this coverage 

was that Asians had higher average risk. As pointed out above, because Asians have 

similar scores, on average, as the population as a whole, scores cannot act as a proxy for 

being Asian. The lack of any proxy effect for property damage liability coverage is made 

very clear in Figure 16, which shows the estimated relationship between claims risk and 

credit-based insurance scores from Table 6. 

Table 6 shows that the results were somewhat different for bodily injury liability, 

collision, and comprehensive coverage. These are the coverages for which African 

Americans and Hispanics had substantially higher average total payouts on claims than 

did non-Hispanic whites. The FTC’s analysis revealed that including these controls did 

reduce somewhat the effect of scores on predicted risk for these three coverages. The 

results show, however, that scores do continue to predict claims strongly if controls for 

race, ethnicity, and income are included in the risk models, which means that scores do 

not predict risk primarily by acting as a proxy for these characteristics. In addition to 

Table 6, the results are presented in Figure 16, which shows the estimated relationship 

between scores and risk, with and without controls for race, ethnicity, and income. 

Controls for race, ethnicity, and income decreased the impact of scores on predicted risk 

 

121 A 95% confidence interval for the difference between the score decile parameter estimates from the two 

models was computed using a bootstrap procedure with 500 replications. Details of the bootstrap 

procedure are provided in Appendix C. 
69

for these coverages most for the lowest credit-based insurance score deciles (where 

African Americans and Hispanics are disproportionately located), and these decreases 

were statistically significant. In short, the FTC’s analysis indicates that credit-based 

insurance scores appear to have some proxy effect for three of the four coverages studied, 

but that this is not the primary source of their relationship with claims risk. In the next 

section, we address the magnitude of the proxy effect. 

b. Magnitude of a Proxy Effect

The FTC also sought to determine the magnitude of any proxy effect from the use 

of credit-based insurance scores. Controlling for race and ethnicity had the largest impact 

on the predicted effect of scores on risk for comprehensive coverage. See columns (g) 

and (h) of Table 6. Without these controls, consumers in the lowest 10% of scores were 

estimated to pose 1.95 times more risk than consumers in the highest 10%. With the 

controls, consumers in the lowest 10% of scores were estimated to pose 1.74 times more 

risk than consumers in the highest 10%. As discussed above, this result should be treated 

with caution, because it could be affected by the lack of a good measure of the 

geographic variation in comprehensive coverage risk. 

Controlling for race and ethnicity had a smaller effect on the predicted impact of 

scores on risk for bodily injury liability and collision coverage. For bodily injury liability 

coverage, without these controls, consumers who are in the lowest 10% of credit-based 

insurance scores were estimated to pose 2.20 times more risk than consumers in the 

highest 10% of scores, while with controls they were estimated to pose only 2.10 times 

more risk. See columns (c) and (d) of Table 6. For collision coverage, without controls, 

consumers who are in the lowest 10% of credit-based insurance scores were estimated to 

pose 2.03 times more risk than consumers in the highest 10% of scores, while with 
70

controls they posed only 1.93 times more risk. See columns (e) and (f) of Table 6. 

It may be difficult to interpret the magnitudes of the proxy effects by examining 

changes in the predicted effects of scores on claims risk. An alternative way to measure 

the magnitude of the proxy effect is to examine how it affects the impact of scores on the 

predicted risk of different race and ethnicity groups. The information presented in Table 

7 compares the impact of scores on predicted risk for different groups, with and without 

race, ethnicity, and income controls. The first column in Table 7 shows that if scores 

were used, then on average the predicted risk of African Americans increased by 10% 

and Hispanics increased by 4.2%, while the predicted risk of non-Hispanic whites 

dropped by 1.6% and Asians dropped 4.9%.122 The second column shows the effects of 

scores on the average predicted risk of the different groups using the impact of scores on 

predicted risk that comes from models that include controls for race, ethnicity, and 

income. When these score effects were used, the average predicted risk of African 

Americans increased by 8.9% and Hispanics by 3.5%, while the predicted risk of nonHispanic whites decreased by 1.4% and Asians by 4.8%.123 The change in the impact of 

scores on predicted risk when race, ethnicity, and income controls were included was 

statistically significant for all racial and ethnic groups. However, given that the use of 

these controls when determining the effects of scores resulted in relatively small 

decreases in the effect of scores on predicted risk for African Americans (10% versus 

 

122 These are the same results that were presented in Table 4. 123 The effects of other variables are held constant between the two models. This was done by using the 

estimated risk effects of non-credit risk variables from the models without race, ethnicity, and income 

controls, and the estimated risk effects of the score deciles from the models with the controls. The 

estimated risk effects of the race, ethnicity, and income controls were not used to predict risk. This hybrid 

risk estimate produced an overall average predicted claims payout that was lower than the actual average 

amount of claims payouts, so every individual’s predicted risk was then inflated by the ratio of actual 

average claims over predicted average claims. 
71

8.9%) and Hispanics (4.2% versus 3.5%), it is apparent that most of the effect of using 

scores on these groups is not because scores act as a proxy for race, ethnicity, and 

income. 

To provide a basis for comparison in evaluating the importance of these proxy 

effects, the FTC conducted the same analysis for several other standard risk variables. 

This could only be done for a small set of the risk variables in the FTC database.124 

Variables that could be used were tenure (number of years the customer has been with the 

company), the model year of the car, and the vehicle identification number (“VIN”), 

which the FTC used to obtain information on vehicle characteristics like body type and 

safety systems.125 In addition, there are two risk variables in the FTC database that did 

not come from the company policy-level database. These are the geographic risk 

measure and the CLUE prior-claims data. 

Table 8 shows the results of applying the FTC’s proxy-effect analysis to these 

variables. The proxy-effect analysis was applied to these other variables in the same way 

it was applied to scores.126 These other variables have much smaller effects on the 

average predicted risk of different racial and ethnic groups than do scores.127 For three of 

 

124 Most of the standard risk variables that came from the companies’ data had large numbers of missing 

values, which reflects the fact that some companies did not collect or store information on some of the 

variables. This means that evaluating these variables is complicated by the fact that when a group of 

policies has “missing” as the value of a given variable, that may mean that most of the policies came from 

the same company. When this is true, the effects of individual variables on risk may be confounded with 

differences across companies in the average risk of their customers. 125 The VINs in the FTC database were truncated, so individual cars cannot be identified. While VIN is 

missing for a substantial number of cars, this is mainly for cars in earlier model years. Newer model years 

have relatively small numbers of missing values, roughly 12%, suggesting that the missing values are 

unlikely to be driven primarily by differences across companies in reporting VINs. 

126 The first column of Table 8 shows the difference in predicted risk between a model that does not include 

the variable being tested and a model that does. The second column shows the difference between a model 

that does not include the variable being tested and a model that does include the variable, where the impact 

of the variable comes from a model that includes controls for race, ethnicity, and income. 127 There are several reasons that could explain why the impacts of these variables on the predicted risk of 

different groups are not as large. It may be because the differences in these variables across groups are not 

(continued) 
72

the four variables, adding race, ethnicity, and income controls reduced the magnitude of 

the impact that the variables had on the change in predicted risk for different groups. 

Adding the geographic risk measure increased average predicted risk 5.4% for African 

Americans, 3.3% for Hispanics, and 4.4% for Asians.128 When controls were included 

for race, ethnicity, and income, the impact of the geographic risk measure decreased to 

4.7% for African Americans, 2.2% for Hispanics, and 3.6% for Asians. The effect of 

tenure on predicted risk for different groups was also reduced by adding race, ethnicity, 

and income controls, from 0.4% to 0.1% for African Americans, from 2.4% to 1.9% for 

Hispanics, and from 2.1% to 1.7% for Asians. Finally, including race, ethnicity, and 

income controls reduced the impact of prior claims on predicted risk from 2.4% to 2.2% 

for African Americans, from 0.3% to 0.2% for Hispanics, and from 1.5% to 1.4% for 

Asians. While these effects are small in absolute value, they are of a similar proportion 

to the effects that these controls have on scores’ impact on the predicted risk of different 

racial and ethnic groups. Thus, like scores, these other risk variables also gain some 

predictive power from acting as proxies for race, ethnicity, or income. 

In summary, the FTC’s analysis shows that credit-based insurance scores do 

predict risk within different racial, ethnic, and income groups. Thus, they do not act 

solely as a proxy for membership in these groups. Scores, however, do gain a small 

amount of additional predictive power because of a proxy effect. Controlling for race 

 as large as the differences for scores, because the impacts of these variables on predicted risk are not as 

large as the impact of scores, and/or because the impact that the inclusion of the variable has on the risk 

associated with other variables is not as large as the impact that scores have. 128 Note that this is not a geographic risk measure used by any company, but rather a variable created for 

the purpose of this study. In addition, the geographic risk measure is not a very effective control for risk on 

comprehensive coverage. A better geographic risk control for comprehensive risk would likely have a 

larger impact on the average predicted risk of African Americans and Hispanics, for comprehensive 

coverage, and thus overall, given the large risk differences between African Americans and Hispanics 

versus non-Hispanic whites for that coverage. 
73

and ethnicity in estimating the relationship between scores and risk causes a small 

reduction in the extent to which scores increase the expected risk of African Americans 

and Hispanics. Finally, this small proxy effect is not limited to scores, but was found for 

three of four other risk variables studied. 

VII. ALTERNATIVE SCORING MODELS

FACTA directed the Commission to determine whether credit-based insurance 

scoring models could be developed that would reduce the differences in scores for 

consumers in protected classes relative to other consumers, yet continue to be effective 

predictors of risk.129 Because race and ethnicity account for the largest differences in 

credit-based insurance scores among groups of consumers in the FTC database, the 

agency focused on constructing an effective model that decreased differences among 

racial and ethnic groups. To the extent practicable, the Commission also sought to build 

an effective model that decreased differences among income groups. 

 As discussed above, credit-based insurance scores are calculated using models 

that assign values to credit history variables to calculate numerical scores. To develop a 

model that effectively predicts risk while reducing differences between racial and ethnic 

groups, the FTC first created a baseline scoring model using the information in its 

database. The Commission chose variables for its baseline model with regard only to 

their power to generate a score that predicts risk as accurately as possible. The FTC then 

used a number of different techniques to try to construct alternative scoring models that 

 

129 FACTA § 215(a)(3) (2006); 15 U.S.C. § 1681 note (2006). 
74

were as predictive as the FTC baseline model, yet had smaller differences in scores 

among racial and ethnic groups. 

The FTC was not able to develop a credit-based insurance scoring model that met 

the dual objectives of maintaining predictive power and decreasing the differences in 

scores between racial, ethnic, and income groups. This does not necessarily mean that a 

model could not be constructed that meets these objectives. It does strongly suggest, 

however, that there is no readily available scoring model or score development 

methodology that would do so. 

 A. The FTC Baseline Model 

Developing a baseline model to use for comparisons is the first step in 

determining whether a model can produce scores that continue to predict risk but have 

smaller differences by race and ethnicity.130 The FTC used claims information in its 

database, the non-credit risk variables in the database, and credit history variables that 

were appended to the insurance policy data to build the model.131 The FTC database 

includes 180 credit history variables for each consumer in the development sample. This 

is a set of variables that ChoicePoint developed over time for its own score-building, and 

they are intended to capture all relevant information in a credit report.132

 

130 Using either the ChoicePoint or FICO model as the base model would not be a useful test. Even a very 

simplistic model developed with the FTC database is likely to do better at predicting claims in the FTC 

database than either of those scores, because it is predicting “within sample.” That is, the model is 

predicting the very claims that were used to develop it. 

131 The development sample was limited to consumers for whom there is race, ethnicity, and income 

information in the FTC database. This demographic information was used only to develop the alternative 

scoring models, not the baseline model. Appendix G contains a description of the methodologies used to 

produce this credit-based insurance score, as well as the other scoring models discussed in this section. 132 No ChoicePoint model uses all 180 variables, and many of these variables are not used in any model. 
75

The Commission selected variables for its baseline model that would produce 

credit-based insurance scores that were effective in predicting total dollars paid out on 

claims per year,133 after controlling for other non-credit risk factors, such as age and 

driving history. This model was constructed without giving any consideration to race, 

ethnicity, or income. Insurance companies and other private firms that develop scoring 

models likewise build their models in a “race blind” fashion. 

The variables that the FTC determined produced scores that were most predictive 

of the claims of the consumers in its development sample are presented in Table 9.134 It 

shows the fifteen variables chosen and the scoring factor assigned to each of them.135 To 

calculate a score for a consumer, the factors for his or her values of each variable are 

multiplied together.136 

The first five variables that enter the model each represent different aspects of a 

credit report: (1) Delinquencies: presence of derogatory information on the file; (2) 

Credit utilization: number of accounts with balance greater than 75% of the credit limit 

or all-time high credit balance; (3) Age of accounts: average age of bank revolving 

(credit card) accounts; (4) Inquiries; and (5) Type of Credit: presence of an open auto 

finance account in the credit report.137 The variables that entered the model later are all 

 

133 The models are intended to be predictive of claims for all major types of coverage. For this reason, 

claims were summed across coverages into a single measure of losses. Claims under first-party medical 

coverage’s, “Med Pay” and personal injury protection, are also included in the “total losses” variable. 134 The variable descriptions are proprietary and confidential information of ChoicePoint. Some variable 

descriptions have been made public previously. For other variables, we include only a general description 

of the type of variable. 

135 The Table shows the variables in the order in which they were chosen by the score-building 

methodology. Variables chosen earlier are generally those that provide greater predictive power to the 

scoring model. 

136 The resulting score is the inverse of the relative predicted risk for the consumer. The inverse is used so 

that higher scores are associated with lower predicted risk. 137 An auto finance account is an account with a lender associated with a car company, like GMAC or Ford 

Motor Credit. 
76

variations on these categories, with the exception of a variable that measures what share 

of credit card accounts on the report are currently reported as “open.” The category with 

the greatest impact on scores is delinquencies, which makes up six of the fifteen 

variables.138

The scores the FTC baseline model produces did predict risk. Figure 17 shows 

the relationship between total claims paid out and the FTC credit-based insurance score 

for the four major types of automobile coverage. Each graph shows three lines: (1) the 

average total amount paid on claims by score decile in the development sample; (2) the 

estimated relationship between scores and claims in the development sample from 

models controlling for other risk factors; and, (3) the average total amount paid on claims 

by score decile in CLUE data for the period June 2001 to December 2001, for people 

who were not in the development sample (an “out of sample” check).139 If the model 

generated scores that effectively predicted risk, then the lines on the graph should slope 

downward to the right. The FTC baseline model produced results consistent with this 

expected pattern. For example, for bodily injury liability coverage, consumers in the 

lowest 10% of scores were more than three and a half times riskier than consumers in the 

highest 10% of scores. Even for property damage liability claims, which have the 

weakest relationship with the FTC score, consumers in the lowest 10% of scores of the 

 

138 In looking over the model, it is important to keep in mind that a piece of information in a credit report 

can be represented in multiple ways and affect multiple variables. This means that care must be taken 

when interpreting some of the results. For example, the score factors for variable C show that 

delinquencies on a particular kind of account actually lead to a better score, which seems very strange in 

isolation. But, it simply means that, in these data, a delinquency on that type of account is less indicative of 

risk than delinquencies on other kinds of accounts, since there is another variable in the model that is a 

broad measure of delinquencies and has a large negative impact on score. 139 The development sample consists only of the sub-sample of the FTC database for which we obtained 

SSA race and ethnicity data. The development sub-sample includes everyone who had a claim in the 

company data, so there was no way to use the company data to look at claims outside of the development 

sample. Instead, we use CLUE data on claims for a different time period. We were able to use data on 

roughly 800,000 policies for these checks. 
77

development sample were more than twice as risky as consumers in the highest 10% of 

scores. Consequently, the FTC baseline model is an effective predictor of risk. Figure 17 

also shows that the FTC baseline model predicts risk for people outside the development 

sample. This result is important in that it shows the FTC baseline model scores do not 

simply predict the claims that were used to develop the model. 

To establish a baseline for evaluating the results of other models, the FTC also 

measured the extent to which its model resulted in differences in scores among racial and 

ethnic groups. Figure 18 shows how the four groups were distributed across the range of 

FTC baseline-model scores. The horizontal axis shows score deciles, and the vertical 

axis shows the share of each group that fell in each decile. The deciles were defined 

using the overall distribution of scores, so if a given group had the same distribution of 

scores as the overall sample, 10% of that group’s population would fall in each decile. 

Figure 18 shows that the FTC baseline model produced lower scores for African 

Americans and Hispanics than for non-Hispanic whites and Asians.140 

Table 9 also shows the breakdown of the different racial and ethnic groups across 

the variables used in the FTC’s baseline model. The variables that show large differences 

across racial and ethnic group are those relating to payment history (e.g., delinquencies) 

and public records, and the variable for the share of accounts with high balances relative 

to credit limits. The inclusion of these variables in the FTC baseline model explains why 

African Americans and Hispanics had lower scores than non-Hispanic whites and Asians. 

 

140 Note that these differences across racial and ethnic groups for the FTC baseline model are very similar 

to those for the ChoicePoint scores discussed above, with the only substantial difference being that Asians 

were less well represented in the higher score categories for the FTC baseline model than for ChoicePoint 

scores. 
78

B. Alternative Scoring Models

1. “Race Neutral” Scoring Models

The FTC credit-based insurance scoring model described in the previous section 

provides a baseline for evaluating alternative models. To construct a model that was 

“neutral” with respect to race, ethnicity, and income, the FTC created a model in which it 

controlled directly for these factors.141 “Neutral” in this context means that while the 

scores produced by the model still may vary across groups, the variables used in the 

scoring model should not derive predictive power from a relationship with race, ethnicity, 

or income. Controls mitigate the impact of credit history variables that differ widely 

among different racial, ethnic, or income groups, if those variables derive a substantial 

portion of their power to predict losses from those differences. If controls are used for 

race, ethnicity, and income, these variables become less predictive of risk. With this loss 

in predictive power, these variables are either not selected for a scoring model at all, or, if 

selected, they are not given as much weight. 

Table 10 shows the scoring model that was produced if controls for race, 

ethnicity, and income were used in the model building process. Most significantly, the 

variables selected in this model that controls for race (a race “neutral” model) are 

extremely similar to those in the FTC baseline model (a race “blind” model). 

Specifically, only two of the fifteen variables are different between these two models, and 

these two particular variables have a relatively weak effect on predicting risk. Despite 

controlling for race, ethnicity, and income, a very similar set of credit history variables 

 

141 Several authors have proposed this approach. See Elaine Fortowsky and Michael Lacour-Little, Credit 

Scoring and Disparate Impact (Dec. 31, 2001), available at

http://fic.wharton.upenn.edu/fic/lacourpaper.pdf; Stephen L. Ross and John Yinger, THE COLOR OF CREDIT: 

MORTGAGE DISCRIMINATION, RESEARCH METHODOLOGY, AND FAIR LENDING ENFORCEMENT (2002). 
79

thus were found to be most predictive of claims. Even though some of these variables 

have large differences across racial and ethnic groups, the variables were chosen not 

because they vary by race, ethnicity, or income. 

The Commission tried another approach to developing a race “neutral” model to 

compare to the FTC baseline model. We constructed a credit-based insurance scoring 

model using a development sample that included only non-Hispanic whites. Because 

there were no other racial or ethnic groups in the sample used to construct such a model, 

the predictive power of the variables selected cannot be attributed to any relationship 

with race or ethnicity. 

Table 11 shows the variables selected when a model was built using only nonHispanic whites as the development sample. Upon first examination, the variables 

selected for this model appear quite different from the variables in the FTC baseline 

model. Eight of the fifteen variables are different, including the variable with the second 

greatest impact. However, there is an important similarity between the variables in these 

two models. The same types of variables were found to be the most important: 

delinquencies, inquiries, measures of high debt burden, age of the credit report, and type 

of credit. 

Both race-neutral models that the FTC developed predict risk within the 

development sample about as well as the FTC baseline model. Figure 19 compares the 

results for each of these models for each of the four types of automobile insurance 

coverage.142 These graphs show that the FTC baseline model (a race blind model) 

produced very similar results for each type of coverage as models that controlled for race 

 

142 Although only non-Hispanic whites were used to develop the “non-Hispanic whites” model, the results 

shown here are for the complete development sample. 
80

and ethnicity or that were developed using only non-Hispanic whites (race neutral 

models). Given the similarity between the types of variables selected for use in these 

models, it is not surprising that these scores have comparable power in predicting risk. 

Just as their risk prediction is comparable to that of the FTC baseline model, the 

race neutral models also display large differences in scores among racial and ethnic 

groups. Figure 20 shows the distribution of scores for the different racial and ethnic 

groups for the two race neutral models and the FTC baseline model. To facilitate 

comparisons, each graph shows the results for all three models for a single racial or 

ethnic group. For all groups except Asians, the distribution of people across deciles was 

nearly identical for the three scoring models. For Asians, the FTC baseline model and 

the model developed using controls for race, ethnicity, and income gave very similar 

results. The model built using only non-Hispanic whites, however, produced a 

distribution of scores for Asians that was more skewed towards lower scores. 

In short, these comparisons show that, although the race neutral models that the 

FTC built accurately predict risk, they do not decrease the differences in credit-based 

insurance scores among racial and ethnic groups. 

2. Model Discounting Variables with Large Differences by Race and 

Ethnicity 

In addition to developing race neutral models as possible alternatives, the FTC 

also constructed alternative models that tried more directly to avoid selecting variables 

with large differences among racial and ethnic groups. In building such models, the FTC 

measured not just how well a given variable predicted claims, but how well it predicted 
81

race and ethnicity. The FTC then chose the variables that contributed the most to 

predicting risk and the least to predicting race and ethnicity.

Table 12 shows one of the models developed using this approach. It is very 

different from the models described in the previous two sections. Most significantly, 

there are no variables that relate directly to delinquencies, which Tables 9 – 11 showed 

varied a great deal among racial and ethnic groups. Most variables selected relate to the 

number and type of accounts that a consumer has. In addition, the discounted model 

includes variables that relate to the age of the credit account and total indebtedness. 

Figure 21 shows that the discounted model is much less predictive of risk than the 

FTC baseline model for each of the four types of automobile insurance coverage. The 

discounted model does produce credit-based insurance scores that predict risk. However, 

each of these graphs shows that the relationship between the credit score and risk is much 

weaker (flatter) for the discounted model than for the FTC baseline model. This shows 

that this process of avoiding variables with large differences between groups resulted in a 

model that is substantially less effective as a predictor of risk than the FTC baseline 

model. 

Figure 22 compares scores for each racial and ethnic group based on the results 

obtained from the discounted model and the FTC baseline model. The model that assigns 

consumers in a racial or ethnic group most closely to 10% in each decile (i.e., a flat line 

at 10% on the vertical axis) shows the least differences based on race and ethnicity. Each 

of these graphs shows that the discounted model resulted in scores with smaller 

differences between members of racial and ethnic minority groups than did the FTC 

baseline model. These differences were most substantial for African Americans. While 

they were still slightly over-represented in the lower score categories, the scores from the 
82

discounted model showed 14% of African Americans are in the bottom 10% of scores. 

The scores from the FTC baseline model, in contrast, showed 27% of African Americans 

in the bottom 10% of scores. Although the discounted model did substantially reduce the 

differences in scores among members of racial and ethnic groups, as discussed above, it 

also provides far less effective risk prediction. 

In summary, the FTC’s inability to build a model that produces scores that 

continues to predict risk accurately at the same time as narrowing the differences in 

scores among racial and ethnic minority groups are by no means definitive. Perhaps 

someone could develop a model that meets both of these objectives. The FTC’s inability 

to build to such a model, however, strongly suggests that there is no readily available 

approach for doing so. 

VIII. CONCLUSION

The FTC’s analysis demonstrates that credit-based insurance scores are effective 

predictors of risk under automobile insurance policies. Using scores is likely to make the 

price of insurance conform more closely to the risk of loss that consumers pose, resulting, 

on average, in higher-risk consumers paying higher premiums and lower-risk consumers 

paying lower premiums. It has not been clearly established why scores are predictive of 

risk. 

Credit-based insurance scores may benefit consumers overall. Scores may permit 

insurance companies to evaluate risk with greater accuracy, which may make them more 

willing to offer insurance to higher-risk consumers. Scores also may make the process of 

granting and pricing insurance quicker and cheaper, cost savings that may be passed on to 
83

consumers in the form of lower premiums. However, little hard data was submitted or 

available to the FTC to quantify the magnitude of these potential benefits to consumers. 

Credit-based insurance scores are distributed differently among racial and ethnic 

groups. The FTC’s analysis revealed that the use of scores for consumers whose 

information was included in the FTC’s database caused the average predicted risk for 

African Americans and Hispanics to increase by 10% and 4.2%, respectively, while it 

caused the average predicted risk for non-Hispanic whites and Asians to decrease by 

1.6% and 4.9%, respectively. These changes in predicted risk are likely to have an effect 

on the insurance premiums that these groups on average pay. 

 Credit-based insurance scores predict risk within racial, ethnic, and income 

groups. Scores have only a small effect as a “proxy” for membership in racial and ethnic 

groups in estimating of insurance risk, remaining strong predictors of risk when controls 

for race, ethnicity and income are included in risk models. The FTC’s analysis revealed 

that the use of scores for consumers whose information was included in the FTC’s 

database caused the average predicted risk for African Americans and Hispanics to 

increase by 10% and 4.2%, respectively. The Commission’s analysis also showed that 

using the effects of scores on predicted risk that come from models that include controls 

for race, ethnicity, and income caused scores to increase the average predicted risk for 

African Americans and Hispanics by 8.9% and 3.5%, respectively. The difference 

between these two predictions for these two groups (1.1% and 0.7%, respectively) shows 

that a relatively small portion of the impact of scores on these groups comes from scores 

acting as a proxy for race, ethnicity, and income. 

Finally, the FTC was not able to develop an alternative credit-based insurance 

scoring model that would continue to predict risk effectively, yet decrease the differences 
84

in scores on average among racial and ethnic groups. This does not mean that a model 

could not be constructed that meets both of these objectives. It does strongly suggest, 

however, that there is no readily available scoring model that would do so. 
TABLES

Performance on Credit Obligations

Late payments/Delinquencies (-)

Collections (generally non-medical) (-)

Public records (judgments or bankruptcies) (-)

Credit-Seeking Behavior

Inquiries (generally non-insurance, non-medical) (-)

New accounts (-)

Use of Credit

Ratio of outstanding balances to available credit (-)

Length of Credit History

Age of oldest account (+)

Average age of all accounts (+)

Types of Credit Used

Department store trade lines (-)

Oil Company trade lines (-)

Travel and Entertainment trade lines (-)

Share of trade lines that are major bank credit cards or mortgages (+)

Note: (-) indicates that high values typically lead to a riskier score, and the converse for (+).

TABLE 1.

Typical Information Used in Credit-Based 

Insurance Scoring Models
Score

Decile

Average Number 

of Claims Per 

Year of Coverage

(per hundred)

Average Cost per 

Claim

Average Total 

Paid on Claims 

Per Year of 

Coverage

[(a) x (b)]

(a) (b) (c)

Property Damage Liability Coverage

 1 5.65 $2,100 $119

 2 4.86 2,119 103

 3 4.51 2,105 95

 4 4.21 2,078 88

 5 4.09 1,982 81

 6 3.85 2,028 78

 7 3.55 2,006 71

 8 3.34 1,994 67

 9 3.40 2,062 70

10 3.17 1,981 63

Overall 4.06 $2,053 $83

Bodily Injury Liablility Coverage

 1 1.79 $8,560 $153

 2 1.59 10,002 159

 3 1.39 7,798 109

 4 1.39 7,993 111

 5 1.19 7,940 95

 6 1.01 8,892 89

 7 0.91 8,538 78

 8 0.89 8,760 78

 9 0.85 9,127 78

10 0.77 8,372 64

Overall 1.18 $8,609 $101

(continued...)

TABLE 2.

Claim Frequency, Claim Severity, and Average Total Amount Paid on Claims
Score

Decile

Average Number 

of Claims Per 

Year of Coverage

(per hundred)

Average Cost per 

Claim

Average Total 

Paid on Claims 

Per Year of 

Coverage

[(a) x (b)]

(a) (b) (c)

Collision Coverage

 1 11.80 $2,364 $279

 2 9.53 2,201 210

 3 8.57 2,174 186

 4 8.09 2,060 167

 5 7.45 2,014 150

 6 6.86 2,057 141

 7 6.47 2,006 130

 8 6.18 1,965 122

 9 6.11 2,003 122

10 5.38 2,004 108

Overall 7.64 $2,112 $161

Comprehensive Coverage

 1 11.50 $1,032 $119

 2 9.69 879 85

 3 9.06 828 75

 4 9.06 773 70

 5 8.34 773 64

 6 8.07 752 61

 7 7.46 774 58

 8 7.42 718 53

 9 7.03 722 51

10 6.95 688 48

Overall 8.44 $807 $68

Source: Analysis of FTC Automobile Insurance Policy Database

TABLE 2.

Claim Frequency, Claim Severity, and Average Total Amount Paid on Claims 

(Continued)

Note: All numbers on this table represent actual means (i.e. , not derived from any risk modelling procedure).
Median Tract 

Income

Median 

Age Percent Male

(a) (b) (c)

African Americans $34,876 46 48%

Hispanics $38,475 42 60%

Asians $50,953 42 72%

Non-Hispanic Whites $44,356 48 68%

Source: Analysis of FTC Automobile Insurance Policy Database

TABLE 3.

Median Income and Age, and Gender Make-Up,

by Race and Ethnicity

Note: Age and gender are measured at the individual level. See section VI.A.2 of the report for a 

discussion of how the age of the individual was determined. Neighborhood income is the median for the 

Census tract where the individual lives. See Appendix C for details on the data sources and the 

construction of the database.
Share With a 

Decrease

Share With 

an Increase

Percent Change in 

Mean Predicted Risk

(a) (b) (c)

African Americans 36% 64% 10.0%

Hispanics 47% 53% 4.2%

Asians 66% 34% - 4.9%

Non-Hispanic Whites 62% 38% - 1.6%

Overall 59% 41% 0.0%

Source: Analysis of FTC Automobile Insurance Policy Database

Note: Predicted change in the amount paid on claims was estimated by comparing individuals' predicted total claims from 

risk models that include ChoicePoint Attract Standard Auto credit-based insurance scores with risk models that do not 

include scores. (By construction, the average of all changes for the sample is zero.) Both of these models include a 

standard set of risk variables as controls, and were run separately for property damage liability, bodily injury liability, 

collision, and comprehensive coverage. In the final step we sum the predicted dollar risks for all four types of insurance 

coverage with and without the use of credit-based insurance scores. See section VI.A.3 of the report for additional details 

on this analysis. Modeling details and a description of the variables included in the models are provided in Appendix D.

TABLE 4.

Change in Predicted Amount Paid on Claims from Using Credit-Based 

Insurance Scores, by Race and Ethnicity
Race and Ethnicity

African Americans 1.01 1.48 * 1.43 * 1.63 *

Hispanics 1.11 1.25 * 1.33 * 1.45 *

Asians 1.17 * 1.11 1.30 * 0.96

Non-Hispanic Whites 1.00 1.00 1.00 1.00

Neighborhood Income

Low 0.97 1.01 1.05 1.16 *

Middle 0.95 * 1.02 0.99 1.06 *

High 1.00 1.00 1.00 1.00

Notes:

Source: Analysis of FTC Automobile Insurance Policy Database

TABLE 5.

Estimated Relative Amount Paid on Claims,

by Race, Ethnicity, and Neighborhood Income

Asterisks indicate statistically significantly different from base category at 5% level.

1) For each variable – i.e. race and ethnicity, and neighborhood income – estimated amount paid on claims per year of 

coverage is measured relative to a base category. For race and ethnicity, the base category is non-Hispanic whites; 

and, for neighborhood income the base category is “high income” neighborhood.

2) Estimated relative amounts paid out on claims per year of coverage for each race, ethnicity and neighborhood income 

category in each column are derived from Tweedie GLMs (Generalized Linear Models); which here include a set of 

standard risk variables as controls, but not score. Since our GLM models are multiplicative, the relativities shown on this 

table are equivalent to the exponentiated regression coefficients of the indicator variables for these categories. Modeling 

details and a description of the variables included in the models are provided in Appendix D.

Comprehensive

Coverage

(d)

Property Damage

Liability Coverage

(a)

Bodily Injury

Liability Coverage

(b)

Collision

Coverage

(c)
Score Decile

1 1.70 * 1.73 * 2.20 * 2.10 *

2 1.52 * 1.53 * 2.14 * 2.07 *

3 1.43 * 1.44 * 1.75 * 1.72 *

4 1.35 * 1.35 * 1.66 * 1.65 *

5 1.24 * 1.24 * 1.37 * 1.36 *

6 1.23 * 1.23 * 1.26 * 1.26 *

7 1.13 * 1.12 * 1.15 1.14

8 1.07 1.07 1.13 1.13

9 1.12 * 1.12 * 1.21 1.21

10 1.00 1.00 1.00 1.00

Race and Ethnicity

African Americans - 0.93 - 1.29 *

Hispanics - 1.06 - 1.15

Asians - 1.20 * - 1.15

Non-Hispanic Whites - 1.00 - 1.00

Neighborhood 

Income

Low - 0.96 - 0.98

Middle - 0.94 * - 1.00

High - 1.00 - 1.00

(continued. . .)

Asterisks indicate statistically significantly different from base category at 5% level.

TABLE 6.

Estimated Relative Amount Paid on Claims, by Score Decile, Race, Ethnicity, 

and Neighborhood Income

Property Damage Liability 

Coverage

Bodily Injury Liability 

Coverage

(a) (b) (c) (d)

Coefficients in dashed boxes are statistically significantly different across models (within a given coverage 

type) at the 5% level.
Score Decile

1 2.03 * 1.93 * 1.95 * 1.74 *

2 1.65 * 1.59 * 1.43 * 1.33 *

3 1.52 * 1.48 * 1.33 * 1.26 *

4 1.39 * 1.36 * 1.28 * 1.23 *

5 1.27 * 1.25 * 1.19 * 1.16 *

6 1.26 * 1.25 * 1.15 * 1.12 *

7 1.16 * 1.15 * 1.12 * 1.10 *

8 1.09 1.08 1.05 1.04

9 1.12 * 1.12 * 1.01 0.99

10 1.00 1.00 1.00 1.00

Race and Ethnicity

African Americans - 1.26 * - 1.46 *

Hispanics - 1.24 * - 1.36 *

Asians - 1.33 * - 0.97

Non-Hispanic Whites - 1.00 - 1.00

Neighborhood 

Income

Low - 1.01 - 1.13 *

Middle - 0.97 - 1.04

High - 1.00 - 1.00

Notes:

Source: Analysis of FTC Automobile Insurance Policy Database

Asterisks indicate statistically significantly different from base category at 5% level.

TABLE 6.

Estimated Relative Amount Paid on Claims, by Score Decile, Race, Ethnicity, 

and Neighborhood Income (Continued)

Coefficients in dashed boxes are statistically significantly different across models (within a given coverage 

type) at the 5% level.

1) For each variable – score, race and ethnicity, and neighborhood income – estimated amount paid on 

claims per year of coverage is measured relative to a base category. For scores, the base category is the 

10th (highest) decile of scores; for race and ethnicity, the base category is non-Hispanic whites; and, for 

neighborhood income the base category is “high income” neighborhood.

2) Estimated relative amounts paid out on claims per year of coverage for each race, ethnicity and 

neighborhood income category in each column are derived from Tweedie GLMs (Generalized Linear 

Models); which here include a set of standard risk variables as controls, as well as score deciles. Since 

our GLM models are multiplicative, the relativities shown on this table are equivalent to the exponentiated 

regression coefficients of the indicator variables for these categories. Modeling details and a description of 

the variables included in the models are provided in Appendix D.

(e) (f) (g) (h)

Collision Coverage Comprehensive Coverage
Average Score Effect From 

Model Without Race, 

Ethnicity, and Income 

Controls

Average Score Effect from 

Model With Race, Ethnicity, 

and Income Controls

(a) (b)

African Americans 10.0% 8.9%

Hispanics 4.2% 3.5%

Asians - 4.9% -4.8%

Non-Hispanic Whites - 1.6% -1.4%

Source: Analysis of FTC Automobile Insurance Policy Database

TABLE 7.

Change in Predicted Amount Paid on Claims from Using CreditBased Insurance Scores Without and With Controls for Race, 

Ethnicity, and Income, by Race and Ethnicity

Notes: 

Column (b): Results in this column are calculated by combining the estimated risk effects of the score 

deciles from models with controls for race, ethnicity, and income with the estimated risk effects of noncredit risk variables from the models used in column (a), which do not include these additional controls. 

The estimated risk effects of race, ethnicity, and income were not used to predict risk. This hybrid risk 

estimate produced an overall average predicted claims payout that was lower than the actual sample 

average amount of claims payouts, so every individual’s predicted risk was then inflated by the ratio of 

actual average claims over predicted average claims.

Column (a): Results in this column come from the same analysis that was used to create Table 4. 

Predicted change in the amount paid on claims was estimated by comparing individual predicted risk 

from risk models that include ChoicePoint Attract Standard Auto credit-based insurance scores with risk 

models that do not include scores. All models include a standard set of risk variables as controls, and 

were run separately for property damage liability, bodily injury liability, collision, and comprehensive 

coverage (in the final step we sum the predicted dollar risks for all four types of insurance coverage); the 

same is true for column (b). This procedure is described in section VI.A.3 of the report. Modeling details 

and a description of the variables included in the models are provided in Appendix D.

Numbers for all race and ethnicity groups are statistically significantly different across the models

in columns (a) and (b) at the 5% level.
Average Effect of Variable 

Without Race, Ethnicity, and 

Income Controls

Average Effect of Variable 

With Race, Ethnicity, and 

Income Controls

(a) (b)

Geographic Risk

African Americans 5.4% 4.7%

Hispanics 3.3% 2.2%

Asians 4.4% 3.6%

Non-Hispanic Whites -1.3% -1.0%

Tenure

African Americans 0.4% 0.1%

Hispanics 2.4% 1.9%

Asians 2.1% 1.7%

Non-Hispanic Whites -0.5% -0.4%

Prior Claims

African Americans 2.4% 2.2%

Hispanics 0.3% 0.2%

Asians 1.5% 1.4%

Non-Hispanic Whites -0.3% -0.3%

(continued...)

TABLE 8.

Change in Predicted Amount Paid on Claims from Using Other Risk Variables, 

Without and With Controls for Race, Ethnicity, and Income, by Race and 

Ethnicity
Average Effect of Variable 

Without Race, Ethnicity, and 

Income Controls

Average Effect of Variable 

With Race, Ethnicity, and 

Income Controls

(a) (b)

Model Year & Other Car Attributes

African Americans -1.0% -1.2%

Hispanics 0.5% 0.5%

Asians 2.8% 2.6%

Non-Hispanic Whites 0.0% 0.0%

Notes: 

Source: Analysis of FTC Automobile Insurance Policy Database

Column (a): Results in this column come from an analysis similar to that used to create Table 4 for score. Predicted change in 

the amount paid on claims was estimated by comparing individual predicted risk from risk models that included the particular 

variable being analyzed here with risk models that did not include the variable. All models include the standard set of risk 

controls (including score), and were run separately for property damage liability, bodily injury liability, collision, and 

comprehensive coverage (in the final step we sum the predicted dollar risks for all four types of insurance coverage); the same

is true for column (b). This procedure is described in section VI.A.3 of the report. Modeling details and a description of the 

variables included in the models are provided in Appendix D.

Column (b): Results in this column are calculated by combining the estimated risk effects of the variable being analyzed from 

models with controls for race, ethnicity, and income with the estimated risk effects of all other risk variables from the models 

used in column (a), which do not include these additional controls. The estimated risk effects of race, ethnicity, and income 

were not used to predict risk. This hybrid risk estimate produced an overall average predicted claims payout that was lower than

the actual sample average amount of claims payouts, so every individual’s predicted risk was then inflated by the ratio of actual 

average claims over predicted average claims.

TABLE 8.

Change in Predicted Amount Paid on Claims from Using Other Risk Variables, 

Without and With Controls for Race, Ethnicity, and Income, by Race and 

Ethnicity (Continued)
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.14 84.5% 56.0% 69.9% 83.0%

1 or more 1.00 15.5% 44.0% 30.1% 17.0%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.25 43.2% 20.3% 28.9% 43.0%

1 - 2 1.16 24.9% 21.8% 24.6% 24.4%

2 - 3 1.09 13.1% 17.3% 15.9% 14.3%

3 - 6 1.04 14.0% 27.6% 23.3% 13.2%

6 or More 1.00 4.8% 13.0% 7.4% 5.1%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.16 3.2% 5.6% 5.5% 2.7%

0 - 24 0.67 3.6% 9.2% 10.0% 6.3%

24 - 51 0.80 10.4% 18.6% 18.6% 16.1%

51 - 64 0.83 9.4% 10.3% 12.0% 11.5%

64 - 99 0.84 34.8% 27.8% 31.3% 36.8%

99 - 205 0.87 36.2% 26.3% 21.5% 25.4%

205 or More 1.00 2.4% 2.1% 1.2% 1.3%

(continued. . .)

TABLE 9.

Baseline Credit-Based Insurance Scoring Model Developed by the FTC 

1) Variable A: Presence of Certain Delinquencies or Adverse Public Records on the Credit File

2) Number of Accounts with Balance Greater than 75% of High Credit (Credit Limit)

3) Average Number of Months Bank Revolving Accounts Have Been Open

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade lines 1.30 34.1% 21.6% 15.9% 19.8%

0 1.31 16.6% 13.9% 14.4% 16.2%

1 - 2 1.29 22.0% 22.8% 20.0% 21.3%

2 - 4 1.20 17.8% 23.2% 25.9% 23.6%

4 - 7 1.13 7.3% 12.6% 16.0% 12.7%

7 or more 1.00 2.4% 5.9% 7.8% 6.4%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.13 90.0% 84.1% 88.0% 85.3%

0 or more 1.00 10.0% 15.9% 12.0% 14.7%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.36 77.1% 47.4% 61.4% 75.8%

1 - 9 1.13 22.2% 50.3% 37.4% 23.6%

10 or more 1.00 0.7% 2.2% 1.2% 0.7%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.99 25.1% 23.8% 20.0% 25.1%

0 0.78 71.8% 67.0% 72.5% 71.4%

1 or more 1.00 3.1% 9.2% 7.5% 3.5%

(continued. . .)

6) Number of Accounts 30 Days Late or Worse in the Last 12 Months

4) Variable B: Relates to the Number of Inquiries on the File

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

5) Number of Open Auto Finance Accounts

7) Variable C: Presence of Delinquencies on a Particular Kind of Account

TABLE 9.

Baseline Credit-Based Insurance Scoring Model Developed by the FTC (Continued)

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.00 25.1% 23.8% 20.0% 25.1%

1 or more 1.17 72.3% 71.2% 74.4% 71.7%

6 or more 1.00 2.5% 5.0% 5.6% 3.2%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.75 5.4% 9.8% 8.9% 4.8%

0 - .135 0.89 2.8% 4.3% 3.5% 2.8%

> .135 1.00 91.7% 85.8% 87.7% 92.3%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.16 83.9% 54.1% 68.9% 82.3%

1 or more 1.00 16.1% 45.9% 31.1% 17.7%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - 6 0.82 31.7% 37.2% 38.3% 37.4%

6 - 9 0.87 15.1% 18.0% 18.0% 15.6%

9 - 20 0.89 26.3% 26.7% 25.0% 26.0%

20 or more 1.00 26.9% 18.1% 18.7% 21.0%

(continued. . .)

Share in each category, by race or ethnicity

10) Variable D: Presence of a Particular Kind of Delinquency on the Account

TABLE 9.

Baseline Credit-Based Insurance Scoring Model Developed by the FTC (Continued)

11) Age of Youngest Account (Months)

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

8) Number of Department Store Accounts

9) Share of all Bank Revolving Accounts that are Open
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.89 0.2% 0.4% 0.5% 0.0%

0 - 3 0.82 3.7% 5.6% 4.6% 3.7%

3 or more 1.00 96.2% 94.1% 94.9% 96.3%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - .02 1.17 94.6% 80.7% 88.4% 93.8%

.02 - .14 1.20 2.7% 9.4% 6.0% 2.8%

> .14 1.00 2.8% 9.9% 5.6% 3.5%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.62 3.0% 5.0% 5.2% 2.4%

0 0.90 90.1% 75.3% 81.8% 89.9%

1 or more 1.00 6.9% 19.8% 12.9% 7.7%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.92 91.6% 93.7% 88.7% 91.2%

0 or more 1.00 8.4% 6.3% 11.3% 8.8%

Notes: 

1) Variables in italics have not been described publicly, and ChoicePoint considers the descriptions of those variables to be 

proprietary information.

2) This scoring model was developed to use credit history information to predict the relative risk posed by individuals, where

risk is defined as expected total dollars that would be paid out on claims in a year. To calculate a score for a given individual 

with this model, the appropriate factors for each of the 15 variables are multiplied together. The resulting product is the 

inverse of the estimated relative riskiness of the individual, based on the individual’s credit history. See Appendix E for a 

detailed discussion of the score-building process.

Share in each category, by race or ethnicity

13) Variable F: A Ratio Relating to Delinquencies

14) Number of Bank Revolving Accounts Ever Bad Debt

15) Number of Open Oil Accounts

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

12) Variable E: Relates to the Number of Accounts in the Credit File

TABLE 9.

Baseline Credit-Based Insurance Scoring Model Developed by the FTC (Continued)

Share in each category, by race or ethnicity
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.14 84.5% 56.0% 69.9% 83.0%

1 or more 1.00 15.5% 44.0% 30.1% 17.0%

2) Number of Accounts with Balance Greater than 75% of High Credit (Credit Limit)

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.26 43.2% 20.3% 28.9% 43.0%

1 - 2 1.17 24.9% 21.8% 24.6% 24.4%

2 - 3 1.11 13.1% 17.3% 15.9% 14.3%

3 - 6 1.05 14.0% 27.6% 23.3% 13.2%

6 or More 1.00 4.8% 13.0% 7.4% 5.1%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.13 3.2% 5.6% 5.5% 2.7%

0 - 24 0.67 3.6% 9.2% 10.0% 6.3%

24 - 51 0.80 10.4% 18.6% 18.6% 16.1%

51 - 64 0.82 9.4% 10.3% 12.0% 11.5%

64 - 99 0.84 34.8% 27.8% 31.3% 36.8%

99 - 205 0.87 36.2% 26.3% 21.5% 25.4%

205 or More 1.00 2.4% 2.1% 1.2% 1.3%

4) Number of Open Auto Finance Accounts

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.12 90.0% 84.1% 88.0% 85.3%

0 or more 1.00 10.0% 15.9% 12.0% 14.7%

(continued. . .)

Share in each category, by race or ethnicity

TABLE 10.

Credit-Based Insurance Scoring Model Developed by the FTC by Including Controls for 

Race, Ethnicity, and Neighborhood Income in the Score-Building Process

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

1) Variable A: Presence of Certain Delinquencies or Adverse Public Records on the Credit File

3) Average Number of Months Bank Revolving Accounts Have Been Open
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade lines 1.30 34.1% 21.6% 15.9% 19.8%

0 1.31 16.6% 13.9% 14.4% 16.2%

1 - 2 1.28 22.0% 22.8% 20.0% 21.3%

2 - 4 1.20 17.8% 23.2% 25.9% 23.6%

4 - 7 1.13 7.3% 12.6% 16.0% 12.7%

7 or more 1.00 2.4% 5.9% 7.8% 6.4%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.35 77.1% 47.4% 61.4% 75.8%

1 - 9 1.14 22.2% 50.3% 37.4% 23.6%

10 or more 1.00 0.7% 2.2% 1.2% 0.7%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.97 25.1% 23.8% 20.0% 25.1%

0 0.78 71.8% 67.0% 72.5% 71.4%

1 or more 1.00 3.1% 9.2% 7.5% 3.5%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.76 5.4% 9.8% 8.9% 4.8%

0 - .135 0.89 2.8% 4.3% 3.5% 2.8%

> .135 1.00 91.7% 85.8% 87.7% 92.3%

(continued. . .)

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

5) Variable B: Relates to the Number of Inquiries on the File

6) Number of Accounts 30 Days Late or Worse in the Last 12 Months

TABLE 10.

Credit-Based Insurance Scoring Model Developed by the FTC by Including Controls for 

Race, Ethnicity, and Neighborhood Income in the Score-Building Process (Continued)

Share in each category, by race or ethnicity

8) Share of all Bank Revolving Accounts that are Open

7) Variable C: Presence of Delinquencies on a Particular Kind of Account

Share in each category, by race or ethnicity
9) Number of Department Store Accounts

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.00 25.1% 23.8% 20.0% 25.1%

1 or more 1.15 72.3% 71.2% 74.4% 71.7%

6 or more 1.00 2.5% 5.0% 5.6% 3.2%

10) Age of Youngest Account (Months)

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - 6 0.81 31.7% 37.2% 38.3% 37.4%

6 - 9 0.87 15.1% 18.0% 18.0% 15.6%

9 - 20 0.89 26.3% 26.7% 25.0% 26.0%

20 or more 1.00 26.9% 18.1% 18.7% 21.0%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - 2 0.89 5.0% 9.9% 6.6% 4.1%

2 or more 1.00 95.0% 90.1% 93.4% 95.9%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.16 83.9% 54.1% 68.9% 82.3%

1 or more 1.00 16.1% 45.9% 31.1% 17.7%

(continued. . .)

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

TABLE 10.

Credit-Based Insurance Scoring Model Developed by the FTC by Including Controls for 

Race, Ethnicity, and Neighborhood Income in the Score-Building Process (Continued)

Share in each category, by race or ethnicity

11) Variable G: Relates to the Number of Accounts in the Credit File

12) Variable D: Presence of a Particular Kind of Delinquency on the Account

Share in each category, by race or ethnicity
13) Number of Open Personal Finance Accounts

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.90 82.0% 66.4% 73.6% 82.9%

0 - 2 0.97 14.9% 24.6% 21.5% 14.2%

2 or more 1.00 3.1% 9.0% 4.9% 2.9%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - .02 1.17 94.6% 80.7% 88.4% 93.8%

.02 - .14 1.20 2.7% 9.4% 6.0% 2.8%

> .14 1.00 2.8% 9.9% 5.6% 3.5%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.62 3.0% 5.0% 5.2% 2.4%

0 0.90 90.1% 75.3% 81.8% 89.9%

1 or more 1.00 6.9% 19.8% 12.9% 7.7%

Notes:

1) Variables in italics have not been described publicly, and ChoicePoint considers the descriptions of those variables to be 

proprietary information.

2) This scoring model was developed to use credit history information to predict the relative risk posed by individuals, where

risk is defined as expected total dollars that would be paid out on claims in a year. To calculate a score for a given individual 

with this model, the appropriate factors for each of the 15 variables are multiplied together. The resulting product is the 

inverse of the estimated relative riskiness of the individual, based on the individual’s credit history. This scoring model was 

developed by including controls for race, ethnicity, and neighborhood income during the process of selecting variables for the 

scoring model, and when estimating the final factors that are applied to the credit history variables. See Appendix E for a 

detailed discussion of the score-building process.

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

15) Number of Bank Revolving Accounts Ever Bad Debt

Share in each category, by race or ethnicity

14) Variable F: A Ratio Relating to Delinquencies

TABLE 10.

Credit-Based Insurance Scoring Model Developed by the FTC by Including Controls for 

Race, Ethnicity, and Neighborhood Income in the Score-Building Process (Continued)
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.23 84.5% 56.0% 69.9% 83.0%

1 or more 1.00 15.5% 44.0% 30.1% 17.0%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade lines 1.25 34.1% 21.6% 15.9% 19.8%

0 - 2 1.25 38.5% 36.7% 34.3% 37.5%

2 or more 1.14 21.5% 29.5% 32.0% 29.3%

5 or more 1.00 6.0% 12.3% 17.8% 13.4%

3) Total Average Debt Burden

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

Invalid past due amount 0.89 0.6% 0.8% 0.9% 0.7%

0 - .19 1.20 41.7% 18.4% 26.2% 44.0%

.19 - .46 1.13 25.5% 22.5% 25.5% 24.7%

.46 - .81 1.06 24.4% 38.8% 33.8% 23.7%

> .81 1.00 7.7% 19.4% 13.6% 6.8%

4) Age of Youngest Account (Months)

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - 6 0.84 31.7% 37.2% 38.3% 37.4%

6 - 14 0.90 30.3% 35.5% 34.4% 31.1%

14 or more 1.00 38.0% 27.3% 27.3% 31.4%

(continued. . .)

Share in each category, by race or ethnicity

TABLE 11.

Credit-Based Insurance Scoring Model Developed by the FTC Using a Sample of Only 

Non-Hispanic White Insurance Customers

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

1) Variable A: Presence of Certain Delinquencies or Adverse Public Records on the Credit File

2) Variable B: Relates to the Number of Inquiries on the File
5) Number of Accounts 30 Days Late in the Last 24 Months

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.15 83.9% 65.3% 73.9% 83.7%

1 or more 1.00 16.1% 34.7% 26.1% 16.3%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.82 5.4% 9.8% 8.9% 4.8%

0 or more 1.00 94.6% 90.2% 91.1% 95.2%

7) Number of Open Auto Finance Accounts

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.10 90.0% 84.1% 88.0% 85.3%

0 or more 1.00 10.0% 15.9% 12.0% 14.7%

8) Average Number of Months Account have been Open

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - 32 0.68 3.8% 6.4% 9.8% 9.1%

32 - 75 0.90 30.5% 42.5% 45.2% 40.5%

75 - 118 0.95 41.7% 34.8% 32.7% 37.2%

118 or more 1.00 24.0% 16.4% 12.3% 13.2%

9) Number of Open Accounts

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - 12 1.10 81.3% 76.0% 76.8% 75.4%

12 or more 1.00 18.7% 24.0% 23.2% 24.6%

(continued. . .)

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

TABLE 11.

Credit-Based Insurance Scoring Model Developed by the FTC Using a Sample of Only 

Non-Hispanic White Insurance Customers (Continued)

6) Share of all Bank Revolving Accounts that are Open
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.28 98.8% 95.4% 97.7% 98.7%

1 or more 1.00 1.2% 4.6% 2.3% 1.3%

11) Ratio of Open Personal Financial Accounts to Total Open Accounts

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.90 82.0% 66.4% 73.6% 82.9%

0 or more 1.00 18.0% 33.6% 26.4% 17.1%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.16 83.9% 54.1% 68.9% 82.3%

1 or more 1.00 16.1% 45.9% 31.1% 17.7%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.90 25.1% 23.8% 20.0% 25.1%

0 0.81 71.8% 67.0% 72.5% 71.4%

1 or more 1.00 3.1% 9.2% 7.5% 3.5%

(continued. . .)

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

10) Variable H: Presence of a Particular Kind of Delinquency on the Account

TABLE 11.

Credit-Based Insurance Scoring Model Developed by the FTC Using a Sample of Only 

Non-Hispanic White Insurance Customers (Continued)

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

12) Variable D: Presence of a Particular Kind of Delinquency on the Account

13) Variable C: Presence of Delinquencies on a Particular Kind of Account
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

Disputed 1.41 0.2% 0.5% 0.5% 0.0%

0 - 2 0.85 2.2% 5.0% 3.3% 2.4%

2 or more 1.00 97.6% 94.6% 96.2% 97.5%

15) Number of Bank Installment Accounts Ever Bad Debt

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.37 44.0% 46.8% 48.0% 49.9%

0 1.37 54.7% 49.5% 49.6% 48.7%

1 or more 1.00 1.3% 3.7% 2.4% 1.3%

Notes: 

1) Variables in italics have not been described publicly, and ChoicePoint considers the descriptions of those variables to be 

proprietary information.

2) This scoring model was developed to use credit history information to predict the relative risk posed by individuals, where

risk is defined as expected total dollars that would be paid out on claims in a year. To calculate a score for a given individual 

with this model, the appropriate factors for each of the 15 variables are multiplied together. The resulting product is the 

inverse of the estimated relative riskiness of the individual, based on the individual’s credit history. This scoring model was 

developed using a development sample of only non-Hispanic white insurance customers. See Appendix E for a detailed 

discussion of the score-building process.

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

14) Variable I: Relates to the Number of Accounts in the Credit File

TABLE 11.

Credit-Based Insurance Scoring Model Developed by the FTC Using a Sample of Only 

Non-Hispanic White Insurance Customers (Continued)
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.22 5.6% 10.0% 8.9% 4.9%

$0 - $1,000 1.34 36.0% 28.6% 34.4% 38.1%

$1000 - $3,000 1.25 20.1% 18.0% 17.9% 20.8%

$3,000 - $14,000 1.14 27.4% 31.9% 29.5% 25.5%

$14,000 or more 1.00 10.9% 11.5% 9.2% 10.7%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.72 0.2% 0.4% 0.5% 0.0%

0 - 3 0.80 3.7% 5.6% 4.6% 3.7%

3 or more 1.00 96.2% 94.1% 94.9% 96.3%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - .14 0.83 2.1% 2.6% 2.3% 1.6%

.14 - .27 0.89 8.3% 9.1% 8.1% 8.7%

.27 or more 1.00 89.6% 88.4% 89.6% 89.7%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.26 90.0% 84.1% 88.0% 85.3%

0 or more 1.00 10.0% 15.9% 12.0% 14.7%

(continued. . .)

TABLE 12.

Credit-Based Insurance Scoring Model Developed by the FTC by Discounting 

Variables with Large Differences Across Racial and Ethnic Groups

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

1) Variable J: Indebtedness on Accounts of a Particular Type

2) Variable E: Relates to the Number of Accounts in the Credit File

3) Share of all Accounts that are Open

4) Number of Open Auto Finance Accounts

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.13 68.5% 68.5% 67.9% 69.4%

0 or more 1.00 31.5% 31.5% 32.1% 30.6%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.85 91.6% 93.7% 88.7% 91.2%

0 or more 1.00 8.4% 6.3% 11.3% 8.8%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.00 91.6% 93.7% 88.7% 91.2%

0 - .0741 0.86 4.6% 4.3% 6.2% 5.3%

.0741 or more 1.00 3.8% 2.1% 5.2% 3.5%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 1.15 79.3% 75.6% 74.7% 74.5%

1 or more 1.00 20.7% 24.4% 25.3% 25.5%

(continued. . .)

TABLE 12.

Credit-Based Insurance Scoring Model Developed by the FTC by Discounting 

Variables with Large Differences Across Racial and Ethnic Groups (Continued)

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

8) Number of Accounts Opened in the Last 3 Months

7) Ratio of Open Oil Accounts to Total Open Accounts

Share in each category, by race or ethnicity

5) Number of Open Bank Installment Accounts

6) Number of Open Oil Accounts

Share in each category, by race or ethnicity
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.06 36.1% 37.2% 34.0% 31.7%

1 - 5 1.06 51.5% 49.0% 52.3% 56.0%

5 or more 1.00 12.4% 13.7% 13.6% 12.3%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

0 - 2 0.88 98.3% 97.8% 98.5% 98.5%

2 or more 1.00 1.7% 2.2% 1.5% 1.5%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.77 5.4% 9.8% 8.9% 4.8%

0 - 6 0.96 77.8% 75.2% 74.6% 68.3%

6 or more 1.00 16.8% 15.0% 16.6% 26.9%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.99 32.0% 31.7% 28.1% 33.2%

0 - .36 0.93 58.9% 59.0% 61.6% 60.0%

.36 or more 1.00 9.1% 9.3% 10.3% 6.8%

(continued. . .)

TABLE 12.

Credit-Based Insurance Scoring Model Developed by the FTC by Discounting 

Variables with Large Differences Across Racial and Ethnic Groups (Continued)

12) Ratio of Open Department Store Accounts to Total Open Accounts

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

10) Age of Last Activity

9) Number of Credit Union Accounts

11) Variable K: Number of Accounts of a Particular Type
Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 1.00 68.5% 68.5% 67.9% 69.4%

0 - .2917 1.10 27.5% 28.3% 28.6% 27.2%

.2917 or more 1.00 4.0% 3.2% 3.5% 3.4%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

$0 - $3,000 0.91 4.7% 6.8% 6.7% 4.6%

$3,000 or more 1.00 95.3% 93.2% 93.3% 95.4%

Category Factor

Non-Hispanic 

Whites

African 

Americans Hispanics Asians

No trade line of this type 0.97 49.8% 48.9% 46.3% 45.1%

0 - .0789 0.95 7.4% 8.9% 8.6% 8.6%

.0789 or more 1.00 42.8% 42.2% 45.1% 46.3%

Notes: 

1) Variables in italics have not been described publicly, and ChoicePoint considers the descriptions of those variables to be 

proprietary information.

2) This scoring model was developed to use credit history information to predict the relative risk posed by individuals, where

risk is defined as expected total dollars that would be paid out on claims in a year. To calculate a score for a given individual 

with this model, the appropriate factors for each of the 15 variables are multiplied together. The resulting product is the 

inverse of the estimated relative riskiness of the individual, based on the individual’s credit history. This scoring model was 

developed by discounting the predictive power of variables that had large differences across racial and ethnic groups, so 

that those variables would be less likely to be chosen by the score-building procedure. See Appendix E for a detailed 

discussion of the score-building process.

13) Ratio of Open Bank Installment Accounts to Total Open Accounts

Share in each category, by race or ethnicity

Share in each category, by race or ethnicity

14) Variable L: Based on Total Available Credit

TABLE 12.

Credit-Based Insurance Scoring Model Developed by the FTC by Discounting 

Variables with Large Differences Across Racial and Ethnic Groups (Continued)

15) Ratio of Open Credit Union Accounts to Total Open Accounts

Share in each category, by race or ethnicity

FIGURES

FIGURE 1.

Estimated Average Amount Paid Out on Claims, 

Relative to Highest Score Decile

Without Controlling for Other Risk Variables

After Controlling for Other Risk Variables

Property Damage 

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint 

Credit-Based Insurance Score

Relative 

Claims Bodily Injury

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint 

Credit-Based Insurance Score

Relative 

Claims

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 2.

Frequency and Average Size (Severity) of Claims, 

Relative to Highest Score Decile

Frequency of Claims

Average Size of Claims (Severity)

Property Damage 

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Frequency 

or Severity

Bodily Injury

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Frequency 

or Severity

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Frequency 

or Severity

Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Frequency

or Severity

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 3.

"CLUE" Claims Data: 

Average Amount Paid Out on Claims, 

Relative to Highest Score Decile

Company Submitted Data (July 2000 - June 2001)

Clue July 2000 - June 2001

Clue July 2001 - December 2001

Property Damage

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims Bodily Injury

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 4.

By Model Year of Car:

Estimated Average Amount Paid Out on Claims, 

Relative to Highest Score Decile 

(Property Damage Liability Coverage)

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint 

Credit-Based Insurance Score

Relative Claims 1992 and Prior

1993-1996

1997 and up

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 5.

Change in Predicted Amount Paid on Claims 

from Using Scores

0.0

3.0

6.0

9.0

12.0

15.0

-30% -20% -10% 0% 10% 20% 30% 40% 50% 60%

Percent Change

% Share of 

Sample

Percent with

Increase: 41%

Percent with

Decrease: 59%

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 6.

The Ratio of Uninsured Motorist Claims to 

Liability Coverage Claims

(1996-2003)

States Allowing the Use of Credit-Based Insurance Scores

States Not Allowing the Use of Credit-Based Insurance Scores

See notes on Figures at the end of this section.

Source: Analysis of data from several National Association of Insurance Commissioners Database Reports.

Property Damage

Liability Coverage

0

0.1

0.2

0.3

0.4

1996 1997 1998 1999 2000 2001 2002 2003

Year

Ratio

Bodily Injury

Liability Coverage

0

0.1

0.2

0.3

0.4

1996 1997 1998 1999 2000 2001 2002 2003

Year

Ratio
FIGURE 7.

Share of Cars Insured through States' "Residual Market" Insurance Programs

(1996-2003)

States Allowing the Use of Credit-Based Insurance Scores

States Not Allowing the Use of Credit-Based Insurance Scores

0%

1%

2%

3%

4%

1996 1997 1998 1999 2000 2001 2002 2003

Year

Percent

See notes on Figures at the end of this section.

Source: Analysis of data from several National Association of Insurance Commissioners Database Reports.
FIGURE 8.

Distribution of Scores,

by Race and Ethnicity 

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

10

Highest

Deciles of ChoicePoint Credit-Based Insurance Scores Scores

Percent

African Americans

Hispanics

Asians

Non-Hispanic Whites

Equal Distribution Line

FIGURE 9.

Distribution of Race and Ethnicity, by Score Decile

0%

20%

40%

60%

80%

100%

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint 

Credit-Based Insurance Scores

Percent

Non-Hispanic Whites African Americans Hispanics Asians

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 10.

Distribution of Scores, 

by Neighborhood Income

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Percent

Low-to-Moderate Income

Middle

High Equal Distribution Line

FIGURE 11.

Distribution of Neighborhood Income, by Score Decile

0%

20%

40%

60%

80%

100%

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Percent

Low-to-Moderate Income Middle Income High Income 

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 12.

Distribution of Scores by Race and Ethnicity, 

After Controlling for Age, Gender, and Neighborhood Income

With Controls

Without Controls

Equal Distribution Line

Non-Hispanic Whites

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Percent African Americans

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Percent

Hispanics

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Percent Asians

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Percent

10

Highest

Scores

10

Highest

Scores

10

Highest

Scores

10

Highest

Scores

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 13.

By Race and Ethnicity:

Change in Predicted Amount Paid on Claims from Using Scores

Non-Hispanic Whites

0

3

6

9

12

15

-30%-20%-10% 0% 10% 20% 30% 40% 50% 60%

Percent Change

% Share 

of Group

Percent with 

Decrease: 62%

Percent with

 Increase: 38%

African Americans

0

3

6

9

12

15

-30% -20%-10% 0% 10% 20% 30% 40% 50% 60%

Percent Change

% Share 

of Group

Percent with 

Decrease: 36%

Percent with

 Increase: 64%

Hispanics

0

3

6

9

12

15

-30% -20%-10% 0% 10% 20% 30% 40% 50% 60%

Percent Change

% Share 

of Group

Percent with 

Decrease: 47%

Percent with

Increase: 53% 

Asians

0

3

6

9

12

15

-30% -20%-10% 0% 10% 20% 30% 40% 50% 60%

Percent Change

% Share 

of Group

Percent with 

Decrease: 66%

Percent with

Increase: 34%

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 14.

By Race and Ethnicity:

Estimated Average Amount Paid Out on Claims, 

Relative to Non-Hispanic Whites in Highest Score Decile 

Property Damage 

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims

Bodily Injury

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims

Non-Hispanic Whites

African Americans

Hispanics

Asians

10

Highest

Scores

10

Highest

Scores

10

Highest

Scores

10

Highest

Scores

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 15.

By Neighborhood Income:

Estimated Average Amount Paid Out on Claims, 

Relative to People in Highest Score Decile in High Income Areas

Property Damage

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims Bodily Injury

Liability Coverage

0.7

1.0

1.3

1.6

1.9

2.2

2.5

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Score

Relative 

Claims

Low-to-Moderate Income

Middle Income

High Income

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 16.

Estimated Average Amount Paid Out on Claims, 

Relative to Highest Score Decile, with and without Controls for Race, Ethnicity, 

and Neighborhood Income

Property Damage

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims Bodily Injury

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1

Lowest

Scores

10

Highest

Scores

Deciles of ChoicePoint

Credit-Based Insurance Scores

Relative 

Claims

Without Race, Ethnicity and Neighborhood Income Controls

With Race, Ethnicity and Neighborhood Income Controls

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 17.

FTC Baseline Model -

Estimated Average Amount Paid Out on Claims, 

Relative to Highest Score Decile

Property Damage

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC

Credit-Based Insurance Scores

Relative 

Claims

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC

Credit-Based Insurance Scores

Relative 

Claims Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC

Credit-Based Insurance Scores

Relative 

Claims

Bodily Injury 

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

3.6

4.0

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC 

Credit-Based Insurance Scores

Relative 

Claims

Within Sample with Controls

Within Sample without Controls

Out of Sample without Controls

Note that the vertical scale on these graphs is different than for previous graphs of relative claims and score deciles.

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 18.

Distribution of FTC Baseline Model Credit-Based Insurance Scores, 

by Race and Ethnicity

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Percent African Americans

Hispanics

Asians

Non-Hispanic Whites

Equal Distribution Line

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 19.

FTC Score Models 

Built Controlling for Race, Ethnicity, and Neighborhood Income:

Estimated Average Amount Paid Out on Claims, 

Relative to Highest Score Decile

Property Damage

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Relative 

Claims

Bodily Injury

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

3.6

4.0

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Relative 

Claims

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Relative 

Claims Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Relative 

Claims

FTC Baseline Model

Model Built with Race, Ethnicity, and Income Controls

Model Built with Non-Hispanic Whites Only

Note that the vertical scale on these graphs is different than for some previous graphs of relative claims and score deciles.

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 20. 

Distribution of FTC Credit-Based Insurance Scores,

by Race and Ethnicity (A)

Non-Hispanic Whites

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Percent African Americans

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Percent

Hispanics

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Percent Asians

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Percent

FTC Baseline Model

Model Built with Race, Ethnicity, and Income Controls

Model Built with Non-Hispanic Whites Only

Equal Distribution Line

10

Highest

Scores

10

Highest

Scores

10

Highest

Scores

10

Highest

Scores

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 21. 

An Additional FTC Credit-Based Insurance Scoring Model:

The "Discounted Predictiveness" Model

Estimated Average Amount Paid Out on Claims, 

Relative to Highest Score Decile

Property Damage

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Relative 

Claims

Bodily Injury

Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

3.6

4.0

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Relative 

Claims

Collision

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Relative 

Claims

Comprehensive

Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1

Lowest

Scores

10

Highest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Relative 

Claims

FTC Baseline Model

“Discounted Predictiveness” Model

Note that the vertical scale on these graphs is different than for some previous graphs of relative claims and score deciles.

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
FIGURE 22.

Distribution of FTC Credit-Based Insurance Scores, 

by Race and Ethnicity (B)

Non-Hispanic Whites

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Percent African Americans

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Percent

Hispanics

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of FTC Credit-Based

Insurance Scores

Percent Asians

0%

5%

10%

15%

20%

25%

30%

1

Lowest

Scores

Deciles of FTC Credit-Base

 Insurance Scores

Percent

FTC Baseline Model

“Discounted Predictiveness” Model

Equal Distribution Line

10

Highest

Scores

10

Highest

Scores

10

Highest

Scores

10

Highest

Scores

See notes on Figures at the end of this section.

Source: Analysis of FTC Automobile Insurance Policy Database
Notes on Figures 

Figure 1: 

The lines labeled “without controlling for other variables” show the actual average 

amount paid out on claims per year of coverage for each score decile, relative to the 

highest score decile. These are derived from the information in Table 2. For example, 

the relativity for the lowest decile on the PD graph has a value of 1.89. This number is 

calculated from column (c) on Table 2; by taking the average total paid on PD claims per 

year of coverage for the 1st decile ($118.73) and dividing it by the respective value for the 

10th decile ($62.70). 

The lines labeled “after controlling for other variables” show the predicted amount paid 

out on claims per year of coverage for each score decile, relative to the highest score 

decile, from Tweedie GLMs (Generalized Linear Models) of claims risk that included 

score and a set of standard risk variables as controls. Since our GLM models are 

multiplicative, the relativities shown by these lines are equivalent to the exponentiated 

coefficients of the score decile indicator variables. Modeling details and a description of 

the variables in the models are provided in Appendix D. 

Figure 2: 

The lines labeled “frequency of claims” show the predicted number of claims per year of 

coverage for each score decile, relative to the highest score decile, from Poisson GLM 

models (“Poisson Regressions”) that included score and a set of standard risk variables as 

controls. Since our GLM models are multiplicative, the relativities shown by these lines 

are equivalent to the exponentiated coefficients of the score decile indicator variables. 

Modeling details and a description of the variables in the models are provided in 

Appendix D. 

The lines labeled “average size of claims” show the predicted average size of claims for 

each score decile, relative to the highest score decile, from Gamma GLM models that 

included score and a set of standard risk variables as controls. Since our GLM model is 

multiplicative, the relativities shown by these lines are equivalent to the exponentiated 

coefficients of the score decile indicator variables. Modeling details and a description of 

the variables in the models are provided in Appendix D. 

Figure 3: 

“CLUE” stands for Comprehensive Loss Underwriting Exchange. This informational/ 

database exchange service is run by ChoicePoint, which collects data on claims from 

most major automobile insurance firms in the United States. These data allow firms to 

determine whether a potential new customer has filed a claim under a previous policy 

with another firm, and use that information in underwriting and rating. 

Each line on this graph shows the average total amount paid out on claims per year of 

coverage for each score decile, relative to the highest decile. These results do not include 

controls for other risk variables because reliable non-credit risk variables are not 

available for the CLUE claims data. For this figure we use the full sample of 1.4 million 
policies, as opposed to the set of policies within the sub-sample of 400,000 normally 

used. This is because the latter would have proved a very limited sub-sample for the 

CLUE analysis for the half a year period moving forward, i.e., for July 2001 to December 

2001. See Appendix C for a description of the company-provided claims data and the 

CLUE database and claims data. 

Figure 4: 

Each line shows the predicted amount paid out on claims per year of coverage for each 

score decile, relative to the highest score decile, for each of three ranges of car model 

years from a Tweedie GLM risk model of claims that included score and a set of standard 

risk variables as controls. The different lines for the three groups of model years were 

estimated by interacting three model year range indicator variables with the score decile 

indicator variables. Modeling details and a description of the variables included in the 

models are provided in Appendix D. 

Figure 5: 

Predicted change in premium was estimated by comparing individuals' predicted total 

claims from risk models that included ChoicePoint Attract Standard Auto credit-based 

insurance score decile indicator variables with risk models that did not include scores. 

(By construction, the average of all changes is zero.) Both of these models were run 

separately for property damage liability, bodily injury liability, collision, and 

comprehensive coverage. In the final step we summed the predicted dollar risks for all 

four types of insurance coverage with and without the use of credit-based insurance 

scores. See section V.A. of the report for additional details on this analysis. Modeling 

details and a description of the variables included in the models are provided in Appendix 

D. 

Figure 6: 

Analysis based on data from several National Association of Insurance Commissioners 

Database Reports. (e.g., National Association of Insurance Commissioners, “Auto 

Insurance Database Report 2003/2004” (2006)) The states included in the category 

“states not allowing the use of credit-based insurance scores” are California, New Jersey, 

Massachusetts, and Hawaii. The category "states allowing the use of credit-based 

insurance scores" includes all other states, except South Carolina and Texas (for which 

complete information was not provided in the NAIC reports). 

Credit-based insurance scores for use in automobile insurance were first commercially 

available in 1995, and were widely adopted by insurance companies (in states that 

allowed their use) during the late 1990s. 

Figure 7: 

The “residual market” consists of state-sponsored programs to sell insurance to drivers 

who are unable to purchase insurance in the normal “voluntary” market. Analysis based 

on data from several National Association of Insurance Commissioners Database 

Reports. (e.g., National Association of Insurance Commissioners, “Auto Insurance 
Database Report 2003/2004” (2006)) The states included in the category “states not 

allowing the use of credit-based insurance scores” are California, New Jersey, 

Massachusetts, and Hawaii. The category "states allowing the use of credit-based 

insurance scores" includes all other states, except South Carolina and Texas (for which 

information was not provided in the NAIC report). 

Credit-based insurance scores for use in automobile insurance were first commercially 

available in 1995, and were widely adopted by insurance companies (in states that 

allowed their use) during the late 1990s. 

Figure 8: 

Each line shows the share of each racial and ethnic group that is in each of the ten deciles 

of the ChoicePoint Attract Standard Auto credit-based insurance score. If each racial and 

ethnic group had the same distribution of scores, 10% of each group would be in each 

decile. 

Figure 9: 

[No Notes] 

Figure 10: 

Each line shows the share of each neighborhood income group that is in each of the ten 

deciles of the ChoicePoint Attract Standard Auto credit-based insurance score. If each 

neighborhood income group had the same distribution of scores, 10% of each group 

would be in each decile. 

Figure 11: 

[No Notes] 

Figure 12: 

Each line shows the share of each racial and ethnic group that is in each of the ten deciles 

of the ChoicePoint Attract Standard Auto credit-based insurance score after controlling 

for age, gender, and neighborhood income. This was calculated based on the residuals 

from an Ordinary Least Squares regression of ChoicePoint Attract Standard Auto creditbased insurance scores on age, gender, and neighborhood income. If each racial and 

ethnic group had the same distribution of scores, after controlling for age, gender, and 

neighborhood income, 10% of each group would be in each decile. 

Figure 13: 

Predicted change in premium was estimated by comparing individuals' predicted total 

claims from risk models that included ChoicePoint Attract Standard Auto credit-based 

insurance scores with risk models that did not include scores. By construction, the 

average of all changes for the entire sample is zero as in Figure 5, but the changes by race 

or ethnic group are not. See note for Figure 5 above or section V.A. of the report for 

additional details on this analysis. Modeling details and a description of the variables 
included in the models are provided in Appendix D. 

Figure 14: 

Each line shows the predicted amount paid out on claims per year of coverage for each 

score decile, relative to non-Hispanic whites in the highest score decile, from a Tweedie 

GLM risk model of claims that included score and a set of standard risk variables as 

controls. These values were generated by interacting the race and ethnicity indicator 

variables with the score decile indicator variables. The score decile cut-points used are 

the same across all race and ethnicity groups (these are the same deciles used for all 

previous Figures). Thus, given the race and ethnicity distributions across score deciles 

observed in Figure 8, there are relatively few African Americans and Hispanics in each of 

the higher score deciles intervals (i.e., fewer than 10% of their group). Modeling details 

and a description of the variables included in the models are provided in Appendix D. 

The differences in the estimates of the amount paid out in claims in higher score deciles 

versus the bottom score decile, within each race group, are generally statistically 

significant (at the 5% level), except for Asians (where they are only significant for 

comprehensive coverage). We also estimated the slope for each race and ethnicity group 

using a continuous score (as opposed to deciles), and found a statistically significant 

downward sloping relationship between score and the amount paid out in claims within 

each group, with the exception of bodily injury and property damage for Asians. 

Property damage for Asians did have a downward slope but was significant only at the 

10% level. Note that Asians are the smallest race or ethnic group in our sample. 

Figure 15: 

Each line shows the predicted amount paid out on claims per year of coverage for each 

score decile, relative to the residents of high-income neighborhoods in the highest score 

decile, from a Tweedie GLM risk model of claims that included score and a set of 

standard risk variables as controls. These values were generated by interacting the 

neighborhood income category indicator variables with the score decile indicator 

variables. Modeling details and a description of the variables included in the models are 

provided in Appendix D. 

Figure 16: 

Each line shows the predicted amount paid out on claims per year of coverage for each 

score decile, relative to the highest score decile, from a Tweedie GLM risk model of 

claims that included score and a set of standard risk variables as controls. Since our 

GLM model is multiplicative, the relativities shown by this line are the exponentiated 

coefficients of the score decile indicator variables. The lines labeled “with race, 

ethnicity, and neighborhood controls” come from a model that also included indicator 

variables for race, ethnicity, and Census tract median income category. Modeling details 

and a description of the variables included in the models are provided in Appendix D. 

Figure 17: 

The line labeled “Within Sample” shows the predicted amount paid out on claims per 
year of coverage for each score decile relative to the highest score decile, of the FTC 

baseline model, from Tweedie GLM risk models of claims that included score and a set 

of standard risk variables as controls. Modeling details and a description of the variables 

included in the models are provided in Appendix D. Details on the score building 

process are provided in Appendix E. 

The line labeled “Within Sample without Controls” shows the average total amount paid 

out on claims per year of coverage for each score decile relative to the highest decile, of 

the FTC baseline model, without controlling for any other risk variables. (This line is 

shown for comparison with the “Out of Sample” values below, for which we do not have 

controls.) 

The “Out of Sample” line is based on CLUE claims data and shows the average total 

amount paid out on claims per year of coverage for each score decile relative to the 

highest decile, of the FTC baseline model, without controlling for any other risk variables 

(since reliable non-credit risk variables are not available in CLUE). This “Out of 

Sample” line is for the period July 2001 to December 2001, and uses CLUE claims data 

only for individuals who were not in the score development sample. 

The development sample consisted only of the sub-sample of the FTC database for which 

we obtained SSA race and ethnicity data, which includes everyone who had a claim in the 

company data, so there is no way to use the company data to look at claims outside of the 

development sample. Therefore, we use CLUE data on claims for a different time period 

and for a different set of people instead (we were able to use data on roughly 800,000 

policies for this from the original 1.4 million dataset). See Appendix C for a description 

of the CLUE database and claims data. Details on the score building process are 

provided in Appendix E. 

(Note that the vertical scale on the graphs in this Figure rises higher than it does for 

previous graphs of relative claims and score deciles in Figures 1-4 and Figures 14-16) 

Figure 18: 

Each line shows the share of each racial and ethnic group that is in each of the ten deciles 

of the scores produced by the FTC’s baseline credit-based insurance scoring model. If 

each racial and ethnic group had the same distribution of scores, 10% of each group 

would be in each decile. 

Figure 19: 

Each line shows the predicted amount paid out on claims per year of coverage for each 

score decile, relative to the highest score decile, from Tweedie GLM risk models of 

claims that included score and a set of standard risk variables as controls. Since our 

GLM models are multiplicative, the relativities shown by these lines are equivalent to the 

exponentiated coefficients of the score decile indicator variables. The lines labeled 

“baseline model” use scores from the FTC baseline scoring model. The lines labeled 

“race, ethnicity, and income controls model” use scores from a model built by controlling 

for those variables during the score building process. The lines labeled “Non-Hispanic 
whites model” come from a scoring model built using a development sample made up 

exclusively of non-Hispanic white insurance customers. Modeling details and a 

description of the variables included in the models are provided in Appendix D. Details 

on the score building process are provided in Appendix E. 

(Note that the vertical scale on the graphs in this Figure rises higher than it does for 

previous graphs of relative claims and score deciles in Figures 1-4 and Figures 14-16) 

Figure 20: 

Each line shows the share of each racial and ethnic group that is in each of the ten deciles 

of three FTC credit-based insurance scoring models. The lines labeled “baseline model” 

use scores from the FTC baseline scoring model. The lines labeled “race, ethnicity, and 

income controls model” use scores from a model built by controlling for those variables 

during the score building process. The lines labeled “Non-Hispanic whites model” come 

from a scoring model built using a development sample made up exclusively of nonHispanic white insurance customers. If each racial and ethnic group had the same 

distribution of scores, 10% of each group would be in each decile. Details on the score 

building process are provided in Appendix E. 

Figure 21: 

Each line shows the predicted relative amount paid out on claims per year of coverage for 

each score decile, relative to the highest score decile, from Tweedie GLM risk models of 

claims that included score and a set of standard risk variables as controls. Since our 

GLM models are multiplicative, the relativities shown by these lines are equivalent to the 

exponentiated coefficients of the score decile indicator variables. The lines labeled 

“baseline model” use scores from the FTC baseline scoring model. The lines labeled 

“discounted predictiveness model” use scores from a model built by discounting the 

power of a variable to predict risk based on how different the variable was across racial 

and ethnic groups. Modeling details and a description of the variables included in the 

models are provided in Appendix D. Details on the score building process are provided 

in Appendix E. 

(Note that the vertical scale on the graphs in this Figure rises higher than it does for 

previous graphs of relative claims and score deciles in Figures 1-4 and Figures 14-16) 

Figure 22: 

Each line shows the share of each racial and ethnic group that is in each of the ten deciles 

of two FTC credit-based insurance scoring models. The lines labeled “baseline model” 

use scores from the FTC baseline scoring model. The lines labeled “discounted 

predictiveness model” use scores from a model built by discounting the power of a 

variable to predict risk based on how different the variable was across racial and ethnic 

groups. If each racial and ethnic group had the same distribution of scores, 10% of each 

group would be in each decile. Details on the score building process are provided in 

Appendix E. 
APPENDIX A 

TEXT OF SECTION 215 OF THE FACT ACT 

 

1

SEC. 215. STUDY OF EFFECTS OF CREDIT SCORES AND CREDITBASEDINSURANCE SCORES ON AVAILABILITY AND AFFORDABILITYOF 

FINANCIAL PRODUCTS. 

(a) STUDY REQUIRED.—The Commission and the Board, in consultation with the 

Office of Fair Housing and Equal Opportunity of the Department of Housing and Urban 

Development, shall conduct a study of— 

(1) the effects of the use of credit scores and credit-based insurance scores on the 

availability and affordability of financial products and services, including credit 

cards, mortgages, auto loans, and property and casualty insurance; 

(2) the statistical relationship, utilizing a multivariate analysis that controls for 

prohibited factors under the Equal Credit Opportunity Act and other known risk 

factors, between credit scores and credit-based insurance scores and the 

quantifiable risks and actual losses experienced by businesses; 

(3) the extent to which, if any, the use of credit scoring models, credit scores, and 

credit-based insurance scores impact on the availability and affordability of credit 

and insurance to the extent information is currently available or is available 

through proxies, by geography, income, ethnicity, race, color, religion, national 

origin, age, sex, marital status, and creed, including the extent to which the 

consideration or lack of consideration of certain factors by credit scoring systems 

could result in negative or differential treatment of protected classes under the 

Equal Credit Opportunity Act, and the extent to which, if any, the use of 

underwriting systems relying on these models could achieve comparable results 

through the use of factors with less negative impact; and 

(4) the extent to which credit scoring systems are used by businesses, the factors 

considered by such systems, and the effects of variables which are not considered 

by such systems. 

(b) PUBLIC PARTICIPATION.—The Commission shall seek public input about the 

prescribed methodology and research design of the study described in subsection (a), 

including from relevant Federal regulators, State insurance regulators, community, civil 

rights, consumer, and housing groups. 

(c) REPORT REQUIRED.— 

(1) IN GENERAL.—Before the end of the 24-month period beginning on the date 

of enactment of this Act, the Commission shall submit a detailed report on the 

study conducted pursuant to subsection (a) to the Committee on Financial 

Services of the House of Representatives and the Committee on Banking, 

Housing, and Urban Affairs of the Senate. 

(2) CONTENTS OF REPORT.—The report submitted under paragraph (1) shall 
2

include the findings and conclusions of the Commission, recommendations to 

address specific areas of concerns addressed in the study, and recommendations 

for legislative or administrative action that the Commission may determine to be 

necessary to ensure that credit and credit-based insurance scores are used 

appropriately and fairly to avoid negative effects. 
APPENDIX B 

REQUESTS FOR PUBLIC COMMENT 

1

[Billing Code 6750-01-P] 

FEDERAL TRADE COMMISSION 

RIN 3084- [AA94] 

Public Comment on Methodology and Research Design for Conducting a Study of 

the Effects of Credit Scores and Credit-Based Insurance Scores on Availability and 

Affordability of Financial Products 

AGENCY: Federal Trade Commission 

ACTION: Notice and request for public comment. 

SUMMARY: The Fair and Accurate Credit Transactions Act of 2003 (“FACT Act” or 

“Act”) requires the Federal Trade Commission (“FTC” or “Commission”) and the 

Federal Reserve Board (“Board”) to conduct a study on the effects of credit scores and 

credit-based insurance scores on the availability and affordability of financial products. 

These products include credit cards, mortgages, auto loans, and property and casualty 

insurance. The Act requires the FTC to seek public input about “the prescribed 

methodology and research design of the study.” As part of its efforts to fulfill its 

obligations under the Act, the FTC seeks public comment on how the FTC and the Board 

should conduct the study. 

DATES: Comments must be received by August 16, 2004. 

ADDRESSES: Public comments are invited, and may be filed with the Commission in 

either paper or electronic form. Comments should refer to “FACT Act Scores Study, 

Matter No. P044804,” to facilitate their organization. A comment filed in paper form 

should include this reference both in the text and on the envelope, and should be mailed 

or delivered to: Federal Trade Commission/Office of the Secretary, Room H-159 (Annex 

N), 600 Pennsylvania Avenue, N.W., Washington, D.C. 20580. The FTC urges that any 

comment filed in paper form be sent by courier or overnight service, if possible, because 
2

U.S. postal mail in the Washington area and at the Commission is subject to delay due to 

heightened security precautions. 

Comments that do not contain any nonpublic information may be filed in 

electronic form (in ASCII format, WordPerfect, or Microsoft Word) as a part of or as an 

attachment to email messages directed to: FACTAscoringstudy@ftc.gov. If a comment 

contains nonpublic information, it must be filed in paper (rather than electronic) form, 

and the first page of the document must be clearly labeled “Confidential.”143

The FTC Act and other laws the Commission administers permit the collection of 

public comments to consider and use in this proceeding as appropriate. All timely and 

responsive public comments, whether filed in paper or electronic form, will be considered 

by the Commission, and will be available to the public on the FTC Web site, to the extent 

practicable, at www.ftc.gov. As a matter of discretion, the FTC makes every effort to 

remove home contact information for individuals from the public comments it receives 

before placing those comments on the FTC Web site. More information, including 

routine uses permitted by the Privacy Act, may be found in the FTC’s privacy policy, at 

http://www.ftc.gov/ftc/privacy.htm. 

FOR FURTHER INFORMATION CONTACT: Jesse Leary, Deputy Assistant 

Director, (202) 326-3480, Division of Consumer Protection, Bureau of Economics, 

Federal Trade Commission, 600 Pennsylvania Avenue, N.W., Washington, DC 20580. 

 

143 Commission Rule 4.2(d), 16 CFR 4.2(d). The comment must also be accompanied by an explicit 

request for confidential treatment, including the factual and legal basis for the request, and must identify 

the specific portions of the comment to be withheld from the public record. The request will be granted or 

denied by the Commission’s General Counsel, consistent with applicable law and the public interest. See 

Commission Rule 4.9(c), 16 CFR 4.9(c). 
3

SUPPLEMENTARY INFORMATION: 

I. Background 

The FACT Act was signed into law on December 4, 2003. Fair and Accurate 

Credit Transactions Act of 2003, Pub. L. No. 108-159 (2003). In general, the Act amends 

the Fair Credit Reporting Act (“FCRA”) to enhance the accuracy of consumer reports and 

to allow consumers to exercise greater control regarding the type and amount of 

marketing solicitations they receive. To promote increasingly efficient national credit 

markets, the FACT Act also establishes uniform national standards in key areas of 

regulation regarding consumer report information. The Act contains a number of 

provisions intended to combat consumer fraud and related crimes, including identity 

theft, and to assist its victims. Finally, the Act requires a number of studies be conducted 

on credit reporting and related issues. 

Section 215 of the FACT Act requires the FTC and the Board, in consultation 

with the Office of Fair Housing and Equal Opportunity of the Department of Housing and 

Urban Development, to conduct a study on the effects of credit scores and credit-based 

insurance scores on the availability and affordability of financial products. These 

products include mortgages, auto loans, credit cards, and property and casualty insurance. 

Section 215 further requires the FTC and the Board to study: 1) “the statistical 

relationship, utilizing a multivariate analysis that controls for prohibited factors under the 

Equal Credit Opportunity Act and other known risk factors, between credit scores and 

credit-based insurance scores and the quantifiable risks and actual losses;” and 2) “the 

extent to which, if any, the use of credit scoring models, credit scores, and credit-based 

insurance scores impact on the availability and affordability of credit to the extent 
4

information is currently available or is available through proxies, by geography, income, 

ethnicity, race, color, religion, national origin, age, sex, marital status, and creed, 

including the extent to which the consideration or lack of consideration of certain factors 

by credit scoring systems could result in negative or differential treatment of the 

protected classes, under the Equal Credit Opportunity Act, and the extent to which, if 

any, the use of underwriting systems relying on these models could achieve comparable 

results through the use of factors with less negative impact.” 

The study is due December 4, 2005. 

II. Request for Comments 

The Act requires the FTC to seek public input about “the prescribed methodology 

and research design of the study.” As part of its efforts to fulfill its obligations under the 

Act, the FTC seeks public comment on how the FTC and the Board should conduct the 

study. Public comment is requested on all aspects of the study. In addition, the FTC seeks 

comment on the following questions: 

1. How should the effects of credit scores and credit based insurance scores on the 

price and availability of mortgages, auto loans, credit cards, other credit products, and 

property and casualty insurance be studied? What is a reasonable methodology for 

measuring the price and availability of mortgages, auto loans, credit cards, other credit. 

Products, and property and casualty insurance, and the impact of credit scores and credit 

based insurance scores on those prices and availability? 

2. An effect can often only be measured relative to a counterfactual (that is, 

relative to some hypothetical alternative situation). To determine the effects of credit 

scores on the price and availability of credit products, what is a reasonable counterfactual 
5

to the current use of credit scores? To determine the effects of credit-based insurance 

scores on the price and availability of property and casualty insurance, what is a 

reasonable counterfactual to the current use of credit-based insurance scores? 

3. Paragraph (a)(2) of Section 215 requires a study of “the statistical relationship, 

utilizing a multivariate analysis that controls for prohibited factors under the 

(ECOA) and other known risk factors, between credit scores and credit-based insurance 

scores and the quantifiable risks and actual losses experienced by businesses.” (The 

ECOA “prohibited factors” are race, color, religion, national origin, sex or marital status, 

and age.) What is an appropriate multivariate technique for studying this relationship? 

What data would be required to undertake such an analysis? What data are available to 

undertake such an analysis? 

4. What is an appropriate methodology to determine whether the use of credit 

scores or credit based insurance scores results in “negative or differential treatment” of 

ECOA-protected classes? 

5. What is an appropriate methodology to determine whether the use of specific 

factors in credit scores or credit based insurance scores results in “negative or differential 

treatment” of ECOA protected classes? 

6. What is an appropriate methodology to determine whether there are factors that 

are not considered by credit scores or credit based insurance scores that result in 

“negative or differential treatment” of ECOA protected classes? 

7. In order to address paragraphs (a)(2) and (a)(3) of Section 215, data are needed 

on the geography, income, ethnicity, race, color, religion, national origin, age, sex, 

marital status, or creed of borrowers, potential borrowers, insurance customers, or 
6

potential insurance customers. Are these data available, and if so, where? 

8. If the data discussed in question 7 are not available, what proxies are available 

for the geography, income, ethnicity, race, color, religion, national origin, age, sex, 

marital status, or creed of borrowers, potential borrowers, insurance customers, or 

potential insurance customers? 

9. If there are proxies for the geography, income, ethnicity, race, color, religion, 

national origin, age, sex, marital status, or creed of borrowers, potential borrowers, 

insurance customers, or potential insurance customers, what type of analysis would allow 

inferences to be drawn using the proxies instead of actual data on individual 

characteristics? What limitations are there to the inferences that can be drawn using 

proxies in place of data on individual characteristics? 

10. One potential proxy for individual characteristics may be Census data about 

the location where a borrower or insurance customer resides. What type of analysis 

would allow inferences to be drawn using data about the characteristics of the location 

where a borrower or insurance customer resides instead of data on individual 

characteristics? What limitations are there to the inferences that can be drawn using data 

about the characteristics of the location where a borrower or insurance customer resides 

in place of data on individual characteristics? 

Authority: Sec. 112(b), Pub. L. 108-159, 117 Stat. 1956 (15 U.S.C. 1681c-1). 

By direction of the Commission. 

 Donald S. Clark 

Secretary 
7

[Billing Code 6750-01-P] 

FEDERAL TRADE COMMISSION 

RIN [3084-AA94] 

Public Comment on Data, Studies, or Other Evidence Related to the Effects of 

Credit Scores and Credit-Based Insurance Scores on the Availability and 

Affordability of Financial Products 

AGENCY: Federal Trade Commission 

ACTION: Notice and request for public comment. 

SUMMARY: The Fair and Accurate Credit Transactions Act of 2003 (“FACT Act” or 

“Act”) requires the Federal Trade Commission (“FTC” or “Commission”) and the 

Federal Reserve Board (“Board”) to conduct a study on the effects of credit scores and 

credit-based insurance scores on the availability and affordability of financial products. 

These products include credit cards, mortgages, auto loans, and property and casualty 

insurance. As part of its efforts to fulfill its obligations under the Act, the FTC seeks 

public comment on any evidence the FTC and the Board should consider in conducting 

the study. 

DATES: Comments must be received by April 25, 2005. 

ADDRESSES: Public comments are invited, and may be filed with the Commission in 

either paper or electronic form. Comments filed in paper form should refer to “FACT 

Act Scores Study” both in the text and on the envelope, to facilitate their organization, 

and should be mailed or delivered to: Federal Trade Commission/Office of the Secretary, 

Room H-159 (Annex Z), 600 Pennsylvania Avenue, N.W., Washington, D.C. 20580. 

The FTC requests that any comment filed in paper form be sent by courier or overnight 

service, if possible, because U.S. postal mail in the Washington area and at the 

Commission is subject to delay due to heightened security precautions. Comments may 
8

be filed in electronic form by clicking on the following: 

https://secure.commentworks.com/FTCCreditScoreStudy/ and following the instructions 

on the web-based form. If a comment contains confidential information, it must be filed 

in paper (rather than electronic) form, and the first page of the document must be clearly 

labeled “Confidential.”144

To ensure that the Commission considers an electronic comment, you must file it 

on the web-based form at https://secure.commentworks.com/FTCCreditScoreStudy/. 

You also may visit http://www.regulations.gov to read this Notice, and may file an 

electronic comment through that website. The Commission will consider all comments 

that regulations.gov forwards to it. 

The FTC Act and other laws the Commission administers permit the collection of 

public comments to consider and use in this proceeding as appropriate. All timely and 

responsive public comments, whether filed in paper or electronic form, will be considered 

by the Commission, and will be available to the public on the FTC Web site, to the extent 

practicable, at www.ftc.gov. As a matter of discretion, the FTC makes every effort to 

remove home contact information for individuals from the public comments it receives 

before placing those comments on the FTC Web site. More Information, including 

routine uses permitted by the Privacy Act, may be found in the FTC’s privacy policy, at 

http://www.ftc.gov/ftc/privacy.htm. 

FOR FURTHER INFORMATION CONTACT: 

 

144 Commission Rule 4.2(d), 16 CFR 4.2(d). The comment must also be accompanied by an explicit 

request for confidential treatment, including the factual and legal basis for the request, and must identify 

the specific portions of the comment to be withheld from the public record. The request will be granted or 

denied by the Commission’s General Counsel, consistent with applicable law and the public interest. See 

Commission Rule 4.9(c), 16 CFR 4.9(c). 
9

Jesse Leary, Deputy Assistant Director, (202) 326-3480, Division of Consumer 

Protection, Bureau of Economics, Federal Trade Commission, 600 Pennsylvania Avenue, 

N.W., Washington, DC 20580. 

SUPPLEMENTARY INFORMATION: 

I. Background 

The FACT Act was signed into law on December 4, 2003. Fair and Accurate 

Credit Transactions Act of 2003, Pub. L. No. 108-159 (2003). In general, the Act amends 

the Fair Credit Reporting Act (“FCRA”) to enhance the accuracy of consumer reports and 

to allow consumers to exercise greater control regarding the type and amount of 

marketing solicitations they receive. The Act contains a number of provisions intended to 

combat consumer fraud and related crimes, including identity theft, and to assist its 

victims. Finally, the Act requires that a number of studies be conducted on credit 

reporting and related issues. 

Section 215 of the FACT Act requires the FTC and the Board, in consultation 

with the Office of Fair Housing and Equal Opportunity of the Department of Housing and 

Urban Development, to conduct a study on the effects of credit scores and credit based 

insurance scores on the availability and affordability of financial products. These 

products include mortgages, auto loans, credit cards, and property and casualty insurance. 

Section 215 further requires the FTC and the Board to study: 1) “the statistical 

relationship, utilizing a multivariate analysis that controls for prohibited factors under the 

Equal Credit Opportunity Act and other known risk factors, between credit scores and 

credit-based insurance scores and the quantifiable risks and actual losses;” and 2) “the 

extent to which, if any, the use of credit scoring models, credit scores, and credit-based 
10

insurance scores impact on the availability and affordability of credit to the extent 

information is currently available or is available through proxies, by geography, income, 

ethnicity, race, color, religion, national origin, age, sex, marital status, and creed, 

including the extent to which the consideration or lack of consideration of certain factors 

by credit scoring systems could result in negative or differential treatment of the 

protected classes, under the Equal Credit Opportunity Act, and the extent to which, if 

any, the use of underwriting systems relying on these models could achieve comparable 

results through the use of factors with less negative impact.” 

The study is due on December 4, 2005. 

II. Request for Comments 

The Act requires the FTC to seek public input about “the prescribed methodology 

and research design of the study.” As part of its efforts to fulfill its obligations under the 

Act, the FTC, (in a Federal Register notice dated June 18, 2004, see 69 FR 34167) sought 

public comment on methodological aspects of the study. The FTC received comments in 

response to that notice, and the FTC and the Board are considering them as they conduct 

the study. In the present request, the FTC seeks comment on specific studies, data, or 

other evidence that might be useful for the study. Although we enumerate a set of 

questions below, we encourage commenters to provide information on any aspects of 

credit scores, credit-based insurance scores, and the effects of scores on the relevant 

markets that would be useful to the study. In particular, the FTC seeks information that 

bears on the following questions: 

A. Credit Scores and Credit:

1. Specifically, how are credit scoring models developed? Who develops credit 
11

scoring models? What data and methodologies are used to develop credit scoring models? 

What factors are used in credit scoring models? Why are those factors used? 

What other factors have been considered for use in credit scoring models, but are not 

used? Why are those other factors not used? Are there benefits or disadvantages, either to 

creditors or consumers, from the use of particular factors by credit scoring models? 

2. How many different credit scoring models are in use today? What different 

types of general purpose or specialized credit scoring models are available? 

Who offers credit scores? 

3. How are credit scores used? Who uses credit scores, and how widely are they 

used? How do they fit into the underwriting process for mortgages, auto loans, credit 

cards, and other credit products? For what purposes are credit scores used, other than the 

initial underwriting or pricing decision? 

4. How has the use of credit scores changed over time? When were they first used 

for each type of financial product (credit cards, mortgages, auto loans, etc.)? How has 

their use expanded to encompass different groups of borrowers (e.g., lower income 

borrowers, urban/rural borrowers, borrowers with poor credit histories, borrowers with 

non-traditional credit histories)? If the use of credit scores has expanded to encompass 

Different groups of borrowers, how has this affected the price or availability of credit to 

those borrowers? 

5. Has the use of credit scores affected the price and availability of mortgages, 

auto loans, credit cards, or other credit products? If so, are there estimates of the type and 

size of such changes? Have some groups of consumers experienced cost reductions while 

others have experienced cost increases? Have some groups of consumers experienced 
12

greater access to credit while others have experienced reduced access? 

6. Has the use of credit scores affected the amount of credit made available to 

consumers? Has it affected initial loan-to-value ratios at which auto loans or mortgages 

(first- or second-lien) are originated to different groups of borrowers? Has it affected 

credit limits on credit cards and home equity lines of credit for different groups of 

borrowers? 

7. How has the use of credit scores affected the costs of underwriting and/or the 

time needed to underwrite? 

8. What impact has the use of credit scores had on the accuracy of underwriting 

decisions? What impact has the use of credit scores had on the share of applicants that are 

approved for mortgages, auto loans, credit cards, or other credit products? What impact 

has the use of credit scores had on the default rates of mortgages, auto loans, credit cards, 

or other credit products? Have the sizes of such changes or effects been estimated and 

reported? 

9. Has the use of credit scores affected the cost and availability of credit to 

consumers with poor credit histories? If so, how? What effect has it had on the use of 

credit by consumers with poor credit histories? 

10. How has the use of credit scores affected the cost and availability of credit to 

consumers with no credit history? What effect has it had on the use of credit by 

consumers with no credit history? 

11. How has the use of credit scores affected refinancing behavior for mortgage, 

auto, or student loans? How has it affected the average life of revolving lines of credit 

(including credit cards)? 
13

12. Has the use of credit scores and credit scoring models impacted the 

availability or cost of credit to consumers by geography, income, ethnicity, race, color, 

religion, national origin, age, sex, marital status, or creed? If so, how has it impacted each 

such category? What are the estimated sizes of any such changes for each of the above 

categories? 

13. To what extent does consideration or lack of consideration of certain factors 

by credit scoring systems result in negative or differential treatment of those categories of 

consumers who are protected under the Equal Credit Opportunity Act (“ECOA”) (e.g., 

race, color, religion, national origin, sex, age, and marital status)? 

14. To what extent, if any, could the use of underwriting systems that rely on 

scoring models achieve comparable results through the use of factors with less negative 

impact on those categories of consumers who are protected under the ECOA? 

15. What steps, if any, do score developers, lenders, or other users of credit scores 

take to ensure that the use of credit scores does not result in negative or differential 

treatment of protected categories of consumers under the ECOA? Have score developers, 

lenders, or other users of credit scores changed the way credit scores are developed or 

used in order to avoid negative or differential treatment of protected categories of 

consumers under the ECOA? Are any particular credit history factors not used because of 

actual or potential negative or differential treatment of protected categories of consumers 

under the ECOA? If so, what are they? 

16. Has the use of credit scores caused a change in the rate of home ownership? 

What is the estimated size of such a change? 

17. Has the use of credit scores caused a change in the method and amount of pre-
14

screening consumers for credit offers? What effects has this had on the terms offered to 

consumers? 

18. What specific role do credit scores play in granting “instant credit?” What 

impact have credit scores had on the availability and use of instant credit? 

19. How has the use of credit scores affected companies' ability to enter new lines 

of business or expand activities in the various credit industries? 

20. What role does credit scoring play in secondary market activities? In what 

ways has the availability of credit scores affected the development of the secondary 

market for credit products? Has the use of credit scoring increased or decreased creditors’ 

access to capital? In what ways? 

21. How are credit scores used to manage existing credit accounts, such as credit 

card accounts? How has the use of credit scores affected the way credit accounts are 

managed? How are credit scores used in the servicing of mortgages, and how has the use 

of credit scores affected the way mortgages are serviced? 

22. How are records of inquiries used by credit scoring systems? Does concern 

about the possible effects on their credit scores affect consumers’ credit shopping 

behavior? If so, what impact does this have on the consumers or on competition in the 

various credit markets? 

23. How does the use of credit scores affect consumers with inaccurate 

information on their credit reports? How does the use of credit scores affect consumers 

who have been the victims of identity theft? 

24. Are there particular forms of inaccuracy or incompleteness in the credit 

reporting system, such as incomplete reporting by creditors, that affect either the 
15

usefulness of credit scores to lenders or the benefits or disadvantages of scoring to 

consumers? What are those types of inaccuracies or incompleteness? How do they affect 

the usefulness of credit scores to lenders or the benefits or disadvantages of scoring to 

consumers? 

B. Credit-Based Insurance Scores and Property and Casualty Insurance:

1. Specifically, how are credit-based insurance scoring models developed? 

Who develops credit-based insurance scoring models? What data and methodologies are 

used to develop credit-based insurance scoring models? What factors are used in credit 

based insurance scoring models? Why are those factors used? What other factors have 

been considered for use in credit-based insurance scoring models, but are not used? Why 

are those other factors not used? Are there benefits or disadvantages, either to insurers or 

consumers, from the use of particular factors by credit-based insurance scoring models? 

2. How many different credit-based insurance scoring models are in use today? 

Who offers credit-based insurance scores? 

3. How are credit-based insurance scores used? Who uses credit-based insurance 

scores, and how widely are they used? How do they fit into the underwriting and rating 

process for automobile and homeowners insurance? 

4. Has the use of credit-based insurance scores affected the price and availability 

of automobile and homeowners insurance? We are especially interested in evidence 

containing estimates of the size of such changes. Have some groups of consumers 

experienced cost reductions while others have experienced cost increases? If so, which 

consumers have experienced reductions and which have experienced increases, and what 

are the magnitudes of those changes? Have some consumers experienced dramatic 
16

increases in their insurance premiums, solely as the result of the introduction of creditbased insurance scoring? If so, what has been the impact of this rise in premiums on these 

consumers? 

5. How has the use of credit-based insurance scores affected the costs of 

underwriting and rating and/or the time needed to underwrite and rate? 

6. How has the use of credit-based insurance scores affected the accuracy of 

underwriting and rating decisions? Have the sizes of such changes been estimated and 

reported? 

7. Has the use of credit-based insurance scores affected the amount of automobile 

or homeowners insurance purchased by consumers? Has it affected the limits or 

deductibles that consumers select when purchasing automobile or homeowners 

insurance? Has it affected the number of drivers who drive without insurance? Has it 

affected the number of homeowners that have no homeowners insurance? What are the 

estimated sizes of such changes? 

8. How has the use of credit-based insurance scores affected the cost and 

availability of automobile or homeowners insurance to consumers with poor credit 

histories? What effect has it had on the purchasing of automobile or homeowners 

insurance by consumers with poor credit histories? 

9. Has the use of credit-based insurance scores affected the cost and availability of 

automobile or homeowners insurance to consumers with no credit history? If so, how? 

What effect has it had on the purchasing of automobile or homeowners insurance by 

consumers with no credit histories? 

10. How has the use of credit-based insurance scores impacted the availability or 
17

cost of insurance to consumers by geography, income, ethnicity, race, color, religion, 

national origin, age, sex, marital status, or creed? What are the estimated sizes of such 

changes for each of the above categories? 

11. To what extent does consideration or lack of consideration of certain factors 

by credit-based insurance scoring systems result in negative or differential treatment of 

protected classes of consumers, that is, the same categories of consumers against whom 

discrimination is prohibited under the ECOA (e.g. race, color, religion, national origin, 

sex, age, and marital status)? 

12. To what extent, if any, could the use of underwriting systems relying on 

credit-based insurance scoring models achieve comparable results through the use of 

factors with less negative impact on consumers in the ECOA protected categories? 

13. What steps, if any, do score developers or insurance companies take to ensure 

that the use of credit-based insurance scores does not result in negative or differential 

treatment of protected categories of consumers listed in the ECOA? Have score 

developers or insurance companies changed the way credit-based insurance scores are 

developed or used in order to avoid negative or differential treatment of protected 

categories of consumers listed in the ECOA? Are any particular credit history factors not 

used because of actual or potential negative or differential treatment of protected 

categories of consumers listed in the ECOA? If so, what are they? 

14. Has the use of credit-based insurance scores caused a change in the method 

and amount of pre-screening consumers for insurance offers? What effects has this had 

on the terms offered to consumers? 

15. How has the use of credit-based insurance scores affected companies’ ability 
18

to enter new lines of the automobile or home- owners insurance business? 

16. If the use of credit-based insurance scores has affected the costs individual 

consumers pay for insurance, has it (i) caused a change in the overall average cost of 

insurance for consumers?; (ii) changed the distribution of individual costs?; or (iii) 

Caused any other change in the costs to consumers? What are the magnitudes of any such 

changes? 

17. Would an analysis of the share or number of consumers that purchase 

automobile or homeowners insurance from “involuntary,” “pooled risk,” “assigned risk,” 

or other types of insurance other than insurance offered on a voluntary basis by private 

insurers, be informative about the price and/or availability of automobile or homeowners 

insurance? Would an analysis of the share of drivers that drive without automobile 

insurance be informative about the price and/or availability of automobile insurance? 

18. What impact, if any, does banning or limiting the use of particular 

underwriting or rating factors, such as gender, territory, or credit-based insurance score, 

have on the price or availability of automobile or homeowners insurance? Has the 

prohibition on the use of credit-based scores for insurance in particular states had any 

impact on the price or availability of automobile or homeowners insurance for consumers 

in those states? If so, what has that impact been? If the use of credit-based insurance 

scores was not allowed in additional states, what impact would this have on the price or 

availability of automobile or homeowners insurance? Are there, or would there be, any 

specific effects on those insurance consumers who are within protected categories listed 

in the ECOA? 

19. How are records of inquiries used by credit-based insurance scoring systems? 
19

Does concern about the possible effects on their credit-based insurance scores affect 

consumers’ insurance-shopping behavior? If so, what impact does this have on 

competition in the insurance markets? 

20. How does the use of credit-based insurance scores affect consumers with 

inaccurate information on their credit reports? How does the use of credit-based 

insurance scores affect consumers who have been the victims of identity theft? 

21. Are there particular forms of inaccuracy or incompleteness in the credit 

reporting system, such as incomplete reporting by creditors, that affect either the 

usefulness of credit-based insurance scores to insurers or the benefits or disadvantages of 

scoring to consumers? What are those types of inaccuracies or incompleteness? How do 

they affect the usefulness of credit-based insurance scores to insurers or the benefits or 

disadvantages of scoring to consumers? 

Authority: Sec. 112(b), Pub. L. 108-159, 117 Stat. 1956 (15 U.S.C. 1681c-1). 

By direction of the Commission. 

 Donald S. Clark 

Secretary 

APPENDIX C 

THE AUTOMOBILE POLICY DATABASE 

1

APPENDIX C. The Automobile Policy Database

The FTC constructed the database of automobile policies used to do the analysis 

for this report by combining policy data from five large auto insurance firms submitted 

with data from a range of additional sources. This Appendix describes that process. 

C.1. The EPIC Database

The automobile policy data in the FTC database were originally collected for a 

study conducted by EPIC, a firm of consulting actuaries, that was released in 2003.145 

The EPIC database was constructed by randomly sampling from the policies in place at 

the participating firms between July 1, 2000 and June 30, 2001. Data on policies that 

were in place throughout the sample year were collected for the entire year. Data on 

policies of customers that left a firm during the year were collected until the policy 

ended, and data on the policies of customers that joined were collected from the date the 

policy began until the end of the year. While the EPIC report did not include information 

on the number of cars in their database, it did provide information on the total “earned car 

years.” An “earned car year” is equivalent to one year of insurance coverage for one car. 

The EPIC database contained roughly 2.7 million earned car years. 

The sampling of policies was done in a way that produced roughly the same 

number of records from each firm. This means that the larger firms in the database are 

under-represented, relative to their market share. All cars covered by a sampled policy 

were included in the sample. The samples were drawn to ensure that some minimum 

number of policies would be available for each state. This means that drivers in small 

 

145 Michael J. Miller and Richard A. Smith, The Relationship of Credit-Based Insurance Scores to Private 

Passenger Automobile Insurance Loss Propensity: An Actuarial Study by EPIC Actuaries, LLC (June 

2003) [hereinafter EPIC Study], available at http://www.progressive.com/shop/EPIC_CreditScores.pdf. 
2

states were over-represented in the sample.146 

EPIC received data on the cars and drivers covered by each policy. Car 

information included vehicle identification number (VIN), miles driven, coverages, 

limits, deductibles, premiums, and claims paid. Driver information included most 

standard risk variables, including age, gender, marital status, and driving history (e.g., 

violations). Important risk variables missing from the data were prior claims (on 

accidents at companies other than the customer’s current company) and territory. EPIC 

did attempt to control for territory in their analysis by using the population density of 

each ZIP code, based on Census data. 

Claims were included in the data if they were for events that occurred between 

July 1, 2000 and June 30, 2001. The samples were drawn in the second half of 2002, and 

information on claims is as of June 30, 2002. For some claims, especially bodily injury 

liability claims, the reported amount paid out on the claim may not reflect the actual 

ultimate cost of the claim. This is because the process of determining the final cost of a 

claim can take a very long time, especially if the claim goes to litigation. For claims that 

were not yet resolved, any reserves for the claims were included as an amount paid. 

Credit-based insurance scores had never been calculated for many of the policies 

in the database. For those that had been scored, different companies may have used 

different models, and the models may have varied by state. The credit scores EPIC 

obtained for the study were ChoicePoint Attract Standard Auto scores. Scores were only 

calculated for one person, the first named insured, for each policy. This means that the 

same score was assigned to each car covered by a policy, even if a different person was 

 

146 All of the analysis presented in the body of the report uses data that have been weighted to be 

geographically representative. 
3

the primary driver of that car.147 Credit history data used by ChoicePoint to calculate 

scores came from the June 2000 archives of Experian (just before the beginning of the 

sample period). There were three possible outcomes for each individual submitted for 

scoring: a score, a “no-hit,” meaning a credit report for the person could not be located in 

Experian’s records, and a “thin-file,” meaning a credit report for the person could be 

located, but it did not contain enough information to calculate a score. 

High-risk drivers are likely under-represented in the database. None of the firms 

provided data on “residual market” policies. These are policies purchased through staterun plans that offer access to insurance for customers who are unable to purchase 

insurance in the normal “voluntary” market. They make up less than 2% of the total 

market for automobile insurance. In addition, while four of the five firms that submitted 

data to the FTC did sell policies to high-risk drivers, two of them did so through 

subsidiaries that did not use the same data systems, and therefore policies from the highrisk subsidiaries were not included in the sample. These subsidiaries represented less 

than 5% of the total business of any one firm, and less than 2% of the total business of the 

five firms. Although these are small portions of these firms’ total customers, it is quite 

possible that the sample under-represents the highest-risk portion of the insurance market. 

For this reason, we conducted an analysis that focused on the highest-risk portion of the 

sample that was collected. This analysis is described in Appendix F. 

C.2 The FTC Database

The database analyzed by the FTC is a subset of the original EPIC database. Not 

all of the firms that contributed data to the EPIC database agreed to have their data 

 

147 This is a form of measurement error that should have the effect of understating the relationship between 

credit score and claims. 
4

forwarded to the FTC for this study. Data from five firms were submitted to the FTC. 

These five firms together represented 27% of the U.S. market of automobile insurance in 

2000 (the time period covered by the data). 

The database submitted by the five firms includes over 2.5 million records. Each 

record has data on one car for up to one year. Many records cover only part of the year, 

either because customers commenced or discontinued coverage during the year, or 

because the company generated a separate record each time a policy was renewed or 

modified. Adjusting for the period of time covered by each record, the total number of 

“car-years” in the database is just over 1.8 million. Many of the policies in the database 

cover more than one car; the total number of policies in the database is 1.4 million. 

The FTC combined the information the insurance firms submitted with data from 

a number of other sources. The agency obtained additional information to broaden the 

range of credit history variables analyzed; to improve the set of other risk controls in the 

analysis; to provide an independent measure of claims; and to analyze issues relating to 

race, ethnicity, income, and national origin. In constructing the database, the FTC never 

took possession of any personally identifying information. The following describes the 

data that were collected and the process by which they were collected. 

 C.2.1 Additional Information Obtained for the Full Sample

Core Policy Data and ChoicePoint Credit Scores 

The participating firms submitted their samples of policy data to EPIC.148 EPIC 

forwarded the data to ChoicePoint. ChoicePoint calculated and appended the Attract 

 

148 During the course of this project, EPIC was purchased by Tillinghast/Towers Perrin Consulting. For 

simplicity, we refer to “EPIC” throughout this appendix, even though some of the steps in the data 

collection and preparation process took place after the change in ownership. 
5

Standard Auto credit-based insurance scores, stripped off the names and addresses, and 

created a new anonymous unique identifier. ChoicePoint then returned the database to 

EPIC. 

EPIC standardized the coding of the data and combined the data from the five 

firms into a single database. When a particular variable was always missing for a 

particular company, a small portion (5%) of records of that variable for other companies 

were chosen at random and changed to missing. This was done to mask which policies 

came from the same company. The combined database was then forwarded to the FTC. 

Territorial Risk Variable 

The five firms also submitted to EPIC data on earned car years and claims on 

property damage liability policies by ZIP code for a three-year period from 2000 to 2002, 

for their full book of business. EPIC combined the data from the five firms to calculate 

ZIP-code level average property damage liability pure premiums (i.e., average dollars 

paid out per year of coverage per car).149 This is an improvement over the original 

Census-based population density measure that EPIC used in its report. The new ZIP code 

risk variable was included in the policy database EPIC forwarded to the FTC. 

Geographic Location Information and Census Data 

ChoicePoint used commercial mapping software to match the addresses of the 

drivers in the database to Census location information (a process commonly referred to as 

“geo-coding”). These data were sent to EPIC, and forwarded to the FTC with the core 

policy database. ChoicePoint was able to determine the Census block location for 95% 

of the overall sample, and 98% of the sub-sample for which Social Security 

 

149 For ZIP codes with fewer than 3,000 property damage liability claims, data from surrounding ZIP codes 

were also used to calculate average pure premiums. 
6

Administration race and ethnicity data were obtained (see below for a discussion of the 

Social Security Administration data). FTC staff used the Census location information to 

append data on race, ethnicity, vehicle ownership, and income from the 2000 Census. 

ChoicePoint Credit History Variables 

In the process of calculating the ChoicePoint credit scores, ChoicePoint generated 

and maintained 180 credit history variables for each person for whom Experian was able 

to locate a credit report. These are a set of variables that ChoicePoint has developed over 

time for its score-building research that are intended to capture all important information 

contained in a credit report. These 180 credit history variables are from the June 2000 

Experian credit report archive. ChoicePoint forwarded the credit history variables 

directly to the FTC. 

CLUE Data 

ChoicePoint collects data on claims from most major automobile insurance firms 

in the United States. These data allow firms to determine whether a potential new 

customer has filed a claim under a previous policy with another firm, and use that 

information in underwriting and rating. The database is referred to as the Comprehensive 

Loss Underwriting Exchange (“CLUE”). 

Pursuant to two 6(b) orders, the FTC obtained the CLUE records for everyone in 

our database for the period July 1995 – June 2003:150 five years prior to the year covered 

by the firm-submitted data, the year covered by the firm data, and two years after. 

 

150 The CLUE database maintains records on individual claims, with name and address and other 

identifying information about the policy on which the claim was filed. The CLUE records that the FTC 

obtained were found by matching the names and addresses in the company-submitted data to the CLUE 

database. Claims, therefore, were only located for people who had the same address in the company data 

and the CLUE database, and the claims of people who had moved were not located. 
7

ChoicePoint sent the CLUE data directly to the FTC. 

Hispanic Surname Match 

ChoicePoint forwarded to Experian a database containing the names and 

addresses of the individuals in the sample, along with the anonymous unique identifier 

created by ChoicePoint. The FTC forwarded to Experian a file containing a list of 

Hispanic surnames created by the Census Department following the 1990 Census.151 

Experian matched the last names of all of the drivers in the database against the list of 

Hispanic surnames. Experian then forwarded directly to the FTC a database containing 

only the anonymous unique identifier for each record in the database, and an indicator for 

whether the surname of the person associated with that record was on the Census list of 

Hispanic surnames. 

Vehicle Characteristics 

Included with the database EPIC forwarded to the FTC was a 10-digit Vehicle 

Identification Number (VIN). These are not enough digits to identify a particular vehicle, 

but enough to identify make and model. The 10-digit VINs were matched to Edmunds 

data on a range of vehicle characteristics, including vehicle body type (e.g., sedan, pickup 

truck, etc.), engine displacement, and safety features. 

 C.2.2 Additional Information Obtained for a Sub-Sample of 400,000

Some data were obtained for only a sub-sample of the records. A sub-sample was 

used for budgetary reasons. The sub-sample consisted of 400,000 of the 1.4 million 

policies in the FTC database. Using a smaller sample can reduce the power of statistical 

tests. To minimize that effect, the sub-sample was drawn using stratification: all policies 

 

151 The list and a paper that describes how it was developed are available at: 

http://www.census.gov/population/documentation/twpno13.pdf 
8

with claims were included in the sub-sample, and policies without claims were sampled 

at a rate sufficient to bring the total to 400,000. 152 This results in a much smaller 

reduction in statistical power than simple, un-stratified random sampling. ChoicePoint 

conducted the sampling following directions from the FTC. 

FICO Scores 

ChoicePoint arranged for Experian to match the names and addresses of the first 

named insureds of the 400,000 policy sub-sample against the June 2000 credit history 

archive, and calculate a FICO “Standard Auto, Greater than Minimum Limits” creditbased insurance score. Experian forwarded the FICO scores (or an indicator for why a 

score could not be calculated – either “no-hit” or “thin file”) directly to the FTC. 

SSA Data on Race, Ethnicity, National Origin, and Gender 

Whenever someone applies for a Social Security card, the Social Security 

Administration (SSA) attempts to collect information on race, ethnicity, national origin, 

and gender. That information is recorded in the SSA’s “Numident” file. Experian 

attempted to locate Social Security Numbers (SSNs) and dates of birth (DOBs) for the 

400,000-person sub-sample in Experian’s consumer credit history files. DOBs were only 

used when an actual day, month, and year could be found. Experian located an SSN or 

valid DOB for 324,563 individuals. The name, SSN, DOB, and the anonymous identifier 

for those individuals were forwarded to the SSA. The SSA matched name, SSN, and 

DOB against the Numident file, and was able to locate information for 308,746 

individuals. The SSA then deleted the names, SSNs, and DOBs, and forwarded to the 

 

152 Of the 400,000, 56% had a claim in at least one coverage, and 44% had no claim. We used the sampling 

probabilities to construct sampling weights, which are used throughout the analysis to keep the sub-sample 

representative of the overall sample. 
9

FTC the anonymous unique identifier and data on race, ethnicity, national origin, and 

gender.
 
APPENDIX D 

MODELING AND ANALYSIS DETAILS

1

APPENDIX D Analysis and Modeling Details

D.1 Intermediate Analysis and Data Preparation

The process of preparing and analyzing the FTC database included several 

intermediate analyses and data preparation steps that require further explanation. First, 

the race and ethnicity data in the database were from several imperfect sources, and were 

combined in a way to take advantage of the strengths of each. Second, the sample likely 

was not representative of the national population of automobile insurance customers, and 

so was weighted to be representative by geography, and race and ethnicity. Finally, the 

risk models were not run on the full sample, mainly because race and ethnicity data are 

only present for a sub-set of the policies. The process of creating the modeling sample is 

described below. 

D.1.1 Using Race and Ethnicity Data

The data on race and ethnicity in the FTC database come from three sources: 

SSA data, a Hispanic surname match, and Census information about the racial and ethnic 

makeup of the location where each individual lives. The SSA data have the two most 

important attributes of race/ethnicity data: they are at the individual level, and they are 

self-reported. The Hispanic surname match is at the individual level, but is not selfreported. (Comparing the SSA data and the Hispanic surname match shows that there are 

many people who have a Hispanic surname who do not report themselves to be Hispanic, 

and vice-versa.) The Census data come from self-reports, but they are only available for 

geographic areas, not for individuals. 

The SSA data do have an important limitation. Prior to 1981, the only available 

answers to the race/ethnicity question were: “White,” “Black,” or “Other.” After 1981, 
2

the choices were expanded to include “Hispanic,” “Asian, Asian-American, or Pacific 

Islander,” and “North American Indian or Native Alaskan,” and the “White” and “Black” 

categories were specifically labeled “non-Hispanic.”153 The “Other” option was dropped. 

Our only option for identifying Hispanics, Asians, and Native Americans among people 

for whom we only had pre-1981 responses was to make inferences using the information 

we did have. 

The SSA was able to locate the records of 308,746 people, out of the 324,563 for 

whom Experian was able to locate an SSN or a valid date of birth. Of those, 10,661 did 

not have a valid response to a race/ethnicity question. Of the 298,085 people for whom 

we had valid race/ethnicity data, 162,755 had only a pre-1981 response. These are the 

people for whom we only had answers for the limited race/ethnicity options. We did, 

however, have pre- and post-1981 responses for 91,519 people. This allows us to 

evaluate how people identified themselves when given the limited set of race/ethnicity 

choices, and how they subsequently identified themselves when given the broader set of 

choices. Based on those patterns, we determined that very few people who answered 

“Black” pre-1981 chose some other option post-1981, and very few people who answered 

“White” pre-1981 chose “Black” post-1981. For this reason, anyone who answered 

“Black” pre-1981 was identified as African American, and no one who answered 

“White” pre-1981 was identified as African American. 

The remaining challenge was to try to determine how someone who answered 

 

153 The post-1981 options raise other concerns. In particular, “Hispanic” is presented as a mutually 

exclusive alternative to the other options. In recent Census questionnaires, “Hispanic/Non-Hispanic” 

information is collected separately from race information. In our data, we find a lower number of people 

with Hispanic surnames self-identifying as Hispanic, post-1981, than does the Census. This is likely due to 

the fact that the Census questionnaire, unlike the SSA questionnaire, collects race and ethnicity data 

separately. 
3

“White” or “Other” pre-1981 would have answered if given the broader post-1981 set of 

choices. We did that using a statistical analysis of individuals for whom we have preand post-1981 responses. The analysis was based on the following factors: the pre-1981 

response (“White” or “Other”); whether someone had a Hispanic surname (from the 

surname match); country of birth (from the SSA data); gender (from the SSA data), and 

the racial/ethnic makeup of the Census block where the person lived. 

We split the group of people who have both a pre-1981 and a post-1981 SSA 

race/ethnicity answer into cells using the following characteristics: 

• Pre-1981 SSA race/ethnicity answer (i.e., “white” or “other”) (2 

categories) 

• Gender (2 categories) 

• Region of Birth, based on Country of Birth from SSA data (4 categories): 

o U.S. born. 

o “Hispanic” Countries: Countries of birth where more than half of 

the people born in that country identified themselves as Hispanic in 

their post-1981 SSA race/ethnicity response. 

o “Asian” Countries: Countries of birth where more than half of the 

people born in that country identified themselves as Asian in their 

post-1981 SSA race/ethnicity response. 

o All Other Countries: Countries of birth that were not included in 

the three prior categories (these are mainly countries in Europe, the 

Middle East, and Africa). 

• Hispanic surname match flag (2 categories) 

This generated 32 cells (i.e., 2x2x4x2). Within each cell, we ran a simple logit 

model to predict the probabilities that someone would answer “Hispanic” vs. “white”, 

“Asian” vs. “white”, or “Black” vs. “white” (the latter only for people who answered 

“Other” pre-1981) using the relative Census block race/ethnicity concentration for that 
4

race/ethnic group vs. non-Hispanic whites as the explanatory variable.154 

We then imputed the probability of being of each race for the individuals in each 

cell for whom we only have a pre-1981 race/ethnicity answer. This was a two-step 

process. We first estimated the probability of being of a given race/ethnicity relative to 

the probability of being non-Hispanic white, and then used a log-odds ratio calculation to 

determine the probability of being of a given race or ethnicity.155

To use the predicted probabilities that come out of this process, we generated a 

record for each race/ethnicity that was estimated to have a positive probability for each 

person. Each of these records was identical, except for the race/ethnicity variable. We 

included the multiple records in the analysis, giving each record a weight equal to the 

predicted probability. For example, someone who is predicted to be non-Hispanic white 

with 85% probability, Asian with 10% probability, and Hispanic with 5% probability will 

have three records in the database. One record will have “non-Hispanic white” as the 

race/ethnicity, and a weight of .85; one record will have “Asian” as the race/ethnicity, 

and a weight of .1; and one record will have “Hispanic” as the race/ethnicity, and a 

weight of .05.156

 

154 For several cells where everyone, or nearly everyone, gave the same post-1981 answer we simply 

assigned everyone in that cell to that category with probability one. For example, all men who answered 

“other” pre-1981, were born in a Hispanic country, and had a Hispanic surname were considered to be 

Hispanic. 

155 The predicted values from a logit are bounded between zero and one, and therefore a logit model gives 

every person for whom we predicted race/ethnicity a positive predicted probability of being each race or 

ethnicity. This is true even for people who lived on blocks with no residents of that race or ethnicity, 

according to the 2000 Census. We therefore reset the predicted probabilities of being of a given race or 

ethnicity to zero if someone lived on a block with no residents of that race or ethnicity and had a predicted 

probability from the logit model of being of that race or ethnicity that was less than 1%. As discussed in 

Appendix F, we also ran the analysis without that restriction and the results were unaffected. 

156 We also estimated the probability of being Native American, but there were so few Native Americans in 

the sample that we did not include them in the analysis. 
5

D.1.2 Nationally Representative Weighting

One limitation of the database is that it was a random sample of policies of 

customers of five insurance firms, not a random sample of all insurance customers in the 

nation. We did not have sufficient information about the automobile insurance market as 

a whole to know exactly how well our sample represented the entire market.157 Because 

much of the analysis presented in this report focuses on the relationship between race, 

ethnicity, income and credit history and insurance risk, the racial, ethnic, and income mix 

of the sample could have affected the results. 

We did not know the racial, ethnic, and neighborhood-income makeup of car 

insurance customers nationwide. We did, however, observe the racial, ethnic, and 

income breakdowns of car ownership, using the 2000 Census. This is shown in column 

(a) of Table A.1.158 Column (b) shows the same breakdowns in our sample.159 

Comparing our sample with Census data on car owners, we see that our sample underrepresented minorities and residents of low-to-moderate income tracts, and overrepresented non-Hispanic whites and residents of upper-income tracts. We did not know 

how much of this difference is due to differences between the customers of the 

companies in the sample relative to the market as a whole, versus differences between the 

racial, ethnic and income make-up of the general population of car owners relative to the 

 

157 We do know that the sample likely under-represents the highest-risk portion of the market. As described 

in Appendix F, the robustness checks appendix, we also estimated risk models for the riskiest segment of 

the sample. 

158 The distribution of race and ethnicity for vehicle owners in the overall Census data, which was used as 

the “target” for the weighting, was adjusted using Census race and ethnicity data for the full sample of 1.4 

million policies and the sub-sample for which we obtained SSA race and ethnicity data. This was done so 

that if the weights developed on the sub-sample were applied to the full sample (which would require 

obtaining SSA race and ethnicity data for the full sample), that full sample would have the correct 

distribution of race and ethnicity. 

159 The racial and ethnic makeup of the FTC sample is based on the SSA race and ethnicity data, including 

the imputed results for people for whom we only have pre-1981 data. 
6

population of car owners with insurance. 

To make our sample close to nationally representative, we weighted the sample 

using a two-step process. We first created a geographic weight at the Census tract level. 

Our database contained cars from most Census tracts in the country. There were 64,946 

tracts with cars in the 2000 Census, and our database contains records from 62,964 of 

those tracts.160 We therefore could make our sample almost perfectly geographically 

representative of the entire country by applying a weight that was the ratio of the share of 

all cars in the country that are in a tract over the share of cars in our sample that are in 

that tract.161 Column (c) of Table A.1 shows the racial, ethnic and income breakdown 

after weighting the sample in this way. The weighted sample was now almost perfectly 

nationally representative by income group, because income is measured at the tract level, 

but minorities were still under-represented. We therefore applied a second weight, which 

was the ratio of the share of cars owned by each racial or ethnic group in the country over 

the share of cars owned by each racial or ethnic group in the sample after applying the 

tract weights. Column (d) of Table A.1 shows the racial, ethnic, and income breakdowns 

after applying those weights.162 The racial and ethnic proportions were now the same as 

those for the nation as a whole, by construction. Adding this second weight did make the 

weighted sample slightly over-representative of residents of low-to-moderate income 

 

160 The 62,964 tracts are in the full database. Tract weights are applied to the full database, and the second 

step – the race-weight step – is done with the sub-sample for whom we have SSA race and ethnicity data. 161 To be precise, the measure in the FTC database is the share of property damage liability earned car years 

by tract. There were a small number of tracts with very small number of earned car years (for example, 

someone may have only had a week of coverage) that resulted in very large tract weights. We capped the 

tract weights at the 99.95 percentile of their distribution. Even with that cap, there were some outliers once 

claims paid were adjusted for earned car years. Removing these outliers did not affect the results of the 

analysis. These results are discussed in Appendix F, the robustness check appendix. 162 Because individuals with imputed race and ethnicity are represented by multiple records in the database, 

each record received the appropriate nationally representative weight associated with the race or ethnicity 

of that record. 
7

tracts, but it was very close to the national numbers. We used these weights throughout 

the analysis, except where noted. 

CLUE data were analyzed using the full sample of 1.4 million policies. Because 

the main weights were developed to apply to SSA race and ethnicity data, and we only 

have SSA race and ethnicity data for a sub-sample, we cannot generate those weights for 

the full sample of 1.4 million. Instead, we first developed a set of weights to make the 

sample geographically representative at the tract level, and then calculated race and 

ethnicity weights based on Census block-level race data. 

D.1.3 The Modeling Sample

Most of the analysis presented in the report was conducted using a sub-sample of 

the original database. As discussed in Appendix C, which describes the construction of 

the database, the FTC only obtained SSA race and ethnicity data for a stratified subsample of the database. Although not all of the analysis required the use of race/ethnicity 

data, the sub-sample with that information was used throughout the report for the sake of 

consistency.163

For a record to be included in the modeling sample, the following conditions had 

to be met: 

• It had to have valid SSA race/ethnicity data. 

• It had to have a Census block location. 

• The combination of coverages on the policy had to be “plausible,” 

meaning the policy had to have one of the following combinations: 

o All four main coverages, or 

o Liability coverages and comprehensive, or 

o Liability coverages only. 

 

163 All of the analyses that did not require race or ethnicity data were also run on the full sample, and all 

results were very similar. These results are discussed in Appendix F. 
8

• For the ChoicePoint score and FICO score risk models, the sample was 

limited to policies with a score. This was done because there were very 

few policies with a “no hit” or “thin file” that had SSA race and ethnicity 

data.164

In addition to the overall analysis sub-sample restrictions, there were additional 

restrictions for the individual coverage risk models: 

• The earned car years for each record for the coverage being modeled had 

to be greater than zero and not greater than one. 

• Total claims count had to be less than six. (This eliminated only a handful 

of records). 

Table A.2 shows summary statistics for the database we analyzed. Column (a) 

shows statistics for the full sample of 1.4 million policies and 2.3 million vehicles. 

Column (b) shows the characteristics of the modeling sub-sample, and column (c) shows 

the characteristics of the modeling sub-sample when weights were applied to make the 

sub-sample nationally representative by geography, race, ethnicity, and income. 

Comparing columns (a) and (b) shows that the sub-sample used for most of the 

analysis did not differ in any dramatic way from the full sample. This similarity is 

reassuring, especially given that some of the steps that produced the sub-sample could be 

quite non-random; in particular, the process of locating Social Security Numbers at 

Experian, which eliminated roughly 1⁄4 of the original sub-sample of 400,000. 

Comparing columns (b) and (c) shows that applying the nationally representative 

weights did affect some of the characteristics of the sub-sample. In particular, the share 

of people with missing values for many of the characteristics was quite different once 

nationally representative weights are applied. The likelihood that a characteristic is 

 

164 We did a separate analysis of “no hits” and “thin files” using Census race/ethnicity data. Those results 

are presented in part V of the report. 
9

“missing” is determined by the information that the data providing firms collected and 

maintained. So, we reasoned that the change in the shares of many of the characteristics 

with unknown values reflected an effect of the nationally representative weights on the 

relative mix of the companies in the sample. As noted in Appendix F, all of the analyses 

were also run without the nationally representative weights, and this had very little effect 

on any of the results. 

D.2 The Risk Models

The statistical models that the FTC constructed and used throughout the report are 

forms of Generalized Linear Models. These are fairly standard modeling techniques in 

the insurance industry. This section describes those techniques, and the specifics of how 

they were used to analyze the FTC database. 

To better understand insurance claims risk, it helps to think of that risk as being 

made up of two components. The first component of risk is the probability that someone 

will file a claim. This is usually called “frequency.” The second component of risk is the 

size of a claim, usually called “severity.” Any risk factor, such as driver experience, 

geography, or credit history, could be correlated with either or both components of 

risk.165 Because claims are generated in this way, claims data have certain distinct 

features. The data consist of a mix of a large number of zeros (policies with no claims) 

and a smaller number of positive dollar amounts. The mass of claims is centered around 

a relatively low number – the hundreds or low thousands of dollars – but claims can 

 

165 Some factors might affect both types of risk in the same direction. For example, someone who drives 

especially fast might be more likely to get into an accident, and any accident would probably be more 

severe than average. Other factors might affect the two types of risk in off-setting ways. For instance, 

someone with a very expensive car might be especially cautious and unlikely to have an accident, but face 

very high repair costs in the event of an accident. 
10

range into the tens or even hundreds of thousands of dollars. Both of these features – the 

many zeros and the long “tail” in the distribution of claims size – require the use of 

specialized statistical techniques. 

There are two approaches to modeling risk. Either frequency and severity can be 

modeled separately, and the results combined, or total claims cost can be modeled in one 

step. Most of the analysis in the body of the report is concerned only with the total 

effects on risk of given variables, such as credit based insurance scores, so most of the 

analysis is done with total claims estimated in a single step. In the discussion of the 

predictive power of scores, however, separate results for frequency and severity are 

presented, as this may provide insights into how scores are predictive of risk. Whether 

risk is modeled in a single step or the two components of risk are modeled separately, the 

standard approaches are all built around Generalized Linear Models (GLMs). 

D.2.1 Generalized Linear Models166

“Generalized Linear Models” are, as the name suggests, a class of statistical 

models that are generalized forms of standard linear models. GLMs generalize from 

linear models by allowing for the dependent variable to be distributed according to any 

member of the exponential family of distributions. GLMs also allow for the variance of 

the error term to vary with the mean of the distribution. Finally, GLMs allow the effects 

of explanatory variables to be a transformation of a linear function. The transformation is 

referred to as the “link function.” A specific GLM model is defined by the link function 

and by the assumption made about the distribution of the dependent variable. The 

 

166 An excellent source for GLMs, especially in the context of modeling insurance claims, is Duncan 

Anderson, et al., A Practitioner’s Guide To Generalized Linear Models, CAS 2004 Discussion Paper 1 – 

116 (May 2005) (presented at Program CAS – Arlington). 
11

standard Ordinary Least Squares regression model is a special case of the GLM, with an 

identity link function and normally distributed errors. 

 D.2.1.1 Modeling “Frequency”

The standard approach to modeling frequency is called “Poisson regression,” 

because it is a Generalized Linear Model (GLM) that uses the Poisson distribution. The 

Poisson distribution gives the likelihood that a certain number of events will occur in a 

given period of time, such as how many claims will be filed on an insurance policy 

during a year. The link function we used in our Poisson regression models was the 

natural log, so the regressions provided estimates of the multiplicative effects of the 

variables on risk. That is, the estimates show the effects of variables on relative risk, so 

an estimated effect of “2” means “predicted claims double when the variable takes this 

value.”167

To implement a Poisson regression with the FTC database for a given coverage, 

the dependent variable was the number of claims for that coverage divided by the earned 

car years of that coverage.168 To limit the effects of outliers, we dropped records that had 

more than six claims on a given coverage in a year.169 Earned car years were also used as 

weights, because records with higher earned car years (that is, records that cover longer 

periods of time) contain more information about risk. Other weights were the sampling 

weights (which are necessary because the modeling sample was a stratified sub-sample of 

 

167 The value “2” here would be the exponentiated coefficient estimate from the regression. 168 Records with a positive count for a coverage but zero dollars paid out on claims for that coverage had 

the count set to zero. It is fairly common for a customer to file a claim that never results in a payment. For 

records with multiple claims and positive dollars paid on claims on a coverage, we cannot determine 

whether all of the claims resulted in payments, as we have only one variable on the total dollars paid on 

claims for each coverage. So, those records may overstate the number of claims that resulted in payments. 169 This restriction caused very few records to be dropped, and it did not affect the results. Appendix F 

includes a discussion of the treatment of outliers. 
12

the original sample), the nationally-representative weights,170 and, where necessary, the 

weights used to implement the race/ethnicity imputation. The explanatory variables in 

the model are listed below. 

 D.2.1.2 Modeling “Severity”

The standard approach to modeling the severity of claims is to use a GLM with a 

Gamma distribution. The Gamma distribution is used because it has the features of the 

observed distribution of claims, all positive values with a relatively low central mass and 

a long tail of larger values. As with the Poisson regressions, we used a natural log link 

function for the Gamma GLM models, so the estimated effects from the model are 

multiplicative. 

To implement a Gamma GLM for a given coverage in the FTC database, the 

sample was first limited to those records with claims on that coverage that resulted in 

payouts. The dependent variable for the severity regression was dollars paid out on 

claims for that coverage divided by the claim count for that coverage.171 The size of 

claims was capped at the 99th percentile to mitigate the effects of outliers. The weights 

were the claim count, the sampling weight, the nationally representative weight, and the 

race-imputation weight, where needed. The explanatory variables were the same as for 

the frequency models, and are described below. 

 D.2.1.2 Modeling Total Claims Cost (“Pure Premiums”)

 

170 All of the analyses were also run without the nationally representative weights. As described in 

Appendix F, this had very little effect on any of the results. 

171 This may be affected by the problem of people with multiple claims on a single coverage, where we 

could not determine if all of the claims resulted in payments. For records with multiple claims and positive 

dollars paid on claims on a coverage, we cannot determine whether all of the claims resulted in payments, 

as we have only one variable on the total dollars paid on claims on a coverage. So, those records may 

overstate the number of claims that resulted in payments, which, in turn, will understate the average claim 

size for that record. 
13

When frequency and severity are modeled separately, the results of the two 

models can be combined and the overall effect of a particular factor on expected dollars 

of claims can be calculated. It is also possible to model claims risk in a single step. This 

can be done by using a GLM with a “Tweedie” distribution.172 The Tweedie distribution 

is a compound distribution of the Poisson and Gamma.173 In essence, the Tweedie GLM 

approach addresses both the frequency effect and severity effect of risk factors in a single 

model. That is, it estimates the effect of a given factor on the total dollars of claims paid 

out per year of coverage. 

To implement the Tweedie GLM with the FTC database for a given coverage, the 

dependent variable is dollars paid out on that coverage divided by earned car years. The 

same restrictions were placed on the dependent variable to limit outliers as were used in 

the frequency and severity models.174 The weights in the pure premiums regressions 

were the same as for the frequency model: earned car years, the sampling weight, the 

nationally representative weight, and, where necessary, the race imputation weight. The 

explanatory variables were the same as those used in the frequency and severity models. 

D.2.2 Bootstrapping Significance Tests

In several places in the analysis, we report the results of statistical significance 

 

172 The distribution is named for M. C. K. Tweedie, who first introduced it in 1984. Tweedie MCK (1984). 

“An index which distinguishes between some important exponential families.” In ‘Statistics Applications 

and New Directions’, Proceedings of the Indian Statistical Institute Golden Jubilee International 

Conference. (Ed. JK Ghosh and J Roy) pp. 579-604. (Indian Statistical Institute: Calcutta). 173 Estimating the Tweedie GLM models required choosing the value of a parameter of the distribution, P, 

that relates to the shape of the distribution and can vary between one and two. A standard approach is to 

use P=1.5, and that was used to produce the results presented in the report. We also tested values of P 

across the range from one to two, and the results of the models were not affected in any meaningful way. 174 Using the same restrictions to avoid outliers did not eliminate all outliers from the pure premium 

models. Even though claim size and the nationally representative weights were capped, several claims 

became outliers when claim size, earned car years, and nationally geographic weights were combined. 

These were not excluded from the results reported in the body of the report. As discussed in Appendix F 

removing those records had no qualitative effects on the results, with one minor exception. 
14

tests. In each case, these tests were done using an approach known as “bootstrapping.”175 

A bootstrap works by repeatedly drawing random samples, with replacement, from the 

analysis sample that are the same size as the analysis sample. Because these “pseudosamples” are drawn with replacement, a record in the analysis sample may appear 

repeatedly, or not at all, in a given pseudo-sample. The parameter of interest is estimated 

for each pseudo-sample, and this is repeated many times. The confidence interval for the 

parameter can then be estimated simply by measuring the observed distribution of 

parameter estimates from all of the pseudo-samples. 

For example, bootstrapping was used to determine whether including race, 

ethnicity, and income controls had a statistically significant impact on the estimated risk 

impact of each score decile. This was done by first generating 500 pseudo-samples by 

drawing samples, with replacement, from the modeling sample.176 The pseudo-samples 

were drawn at the policy level, so that any correlation in the unobserved risk across cars 

on the same policy would be accounted for in the bootstrapped confidence intervals. 

Once the pseudo-samples were generated, the risk models were estimated for each 

pseudo-sample, with and without controls for race, ethnicity, and income. The difference 

between the estimated risk for each score decile for the models with and without the 

controls was computed. Those differences are collected, and form the estimated 

distribution of the difference for each score decile. The 95% confidence interval for the 

difference for a given score decile can then be determined simply by measuring the value 

of the 2.5 percentile and 97.5 percentile of that distribution of estimated differences. 

 

175 A standard reference for the bootstrap is B. Efron and R. Tibshihanit, An Introduction to the Bootstrap, 

1993, Chapman and Hall, Monographs on Statistics and Applied Probability (1993), at 57. 

176 The number of pseudo-samples is arbitrary. We found that our confidence intervals converged after 200 

to 300 replications. 
15

We also calculated robust standard errors for the parameter estimates of the GLM 

Tweedie models that took account of the fact that many records come from the same 

policies (i.e.,“clustering”). The resulting standard errors for the parameters of the models 

were very similar to those produced by the bootstrapping procedure. We rely on the 

bootstrap procedure, however, because statistical significance tests on the parameter 

estimates across different models cannot be done using the standard errors from those 

models (e.g., comparing score decile parameter estimates across models with and without 

controls for race, ethnicity, and income). 

D.2.3 Variables Used in the Risk Models

The following variables were used in the risk models. All variables were included 

in all models, except where otherwise indicated. All variables entered the models as 

indicator (“dummy”) variables. A number of variables have “missing” as one of the 

categories, and this category was included in the models. Whether a variable was 

missing for a record was determined by whether the company that provided that record 

had collected and maintained the information, and therefore when multiple records are 

missing the same variable it may mean they came from the same company. This 

complicates the interpretation of some variables, but may have the benefit of acting like 

an indicator variable for a particular company. 

Credit-Based Insurance Score Decile 

The credit-based insurance score decile of the score on the policy. Deciles were 

determined using property damage liability coverage earned car years as a weight, so 

each decile contains 10% of the property damage liability earned car years. The 

nationally representative weights were used when the score deciles were determined, and 
16

the same decile cut-points were used throughout the analysis. 

Race/Ethnicity 

Race and ethnicity category – from the SSA data, census data, and Hispanic 

surname match. As discussed above, this is a simple indicator variable for people for 

whom we have a post-1981 SSA race/ethnicity response, or who responded “Black” pre1981. Individuals for whom we had only a pre-1981 response which was either “White” 

or “Other,” have separate records for each race or ethnicity that had a positive estimated 

probability, with a weight equal to the estimated probability. Race or ethnicity was 

included in models only where indicated. 

Tract-Level Income 

The median tract income relative to the Metropolitan Statistical Area median 

income. This variable takes the values of less than 80% of the MSA median (“low 

income”), 80% and greater but less than 120% of the MSA median (“middle income”), 

and 120% of the MSA median and greater (“high income”). Income was only used 

where indicated. 

Age / Gender / Marital Status 

The effects of age, gender, and marital status are all inter-dependent. The effect 

of age on risk varies with gender and marital status, the effect of gender on risk varies 

with age and marital status, etc. Fully interacting the three variables, however, leads to 

literally hundreds of possible combinations. To reduce the set of controls used in the 

models, we created groupings of age/gender/marital status that were of similar risk. We 

first created a set of seven age ranges. The age ranges were determined by estimated 

frequency risk models with varying age bands, which in turn were based in part on the 
17

public rate filings of several firms. The chosen categories were interacted with gender 

and marital status. Because gender could take three values (male, female, unknown) and 

marital status could take four values (single, married, divorced or widowed, unknown), 

this produced a total of 7x3x4=84 cells. We then ran risk models for each of the four 

major coverages using all 84 cells. The results showed that the effects on risk of the 

age/gender/marital status categories were fairly similar across the accident-related 

coverages (the liability coverages and collision), but somewhat different for 

comprehensive coverage. We therefore created two sets of age/gender/marital status 

categories. After examining the estimated risk effects of the 84 cells, we created nine 

risk categories for the accident-related coverages and six categories for comprehensive. 

This was done based on the predicted risk for the 84 cells, with attention paid to creating 

“reasonable” categories made up of cells that were “close” to each other on a grid of 

age/gender/marital status. 

Territorial Risk 

The territorial risk variable was calculated by EPIC using three-year average 

property damage liability claims for the five companies, by ZIP-code. This is described 

in more detail in Appendix C. Territorial risk entered the model as quintiles, five groups 

that each contain 20% of the vehicles in the sample, weighted by property damage 

liability coverage earned car years. As described in Appendix F, using deciles instead of 

quintiles did not affect the risk models. 

CLUE Data 

The CLUE data contains information on the number and size of claims for the full 

range of coverages. Several variables were used to capture that information for inclusion 
18

in the risk models.177

CLUE Data – Prior Uninsured Motorist / Underinsured Motorist Claims 

The number of claims that involved an uninsured or underinsured motorist claim 

with a positive dollar value in the prior three years. It takes the values of “0” and “1 or 

more.” 

CLUE Data – Prior Bodily Injury / Property Damage Claims 

The number of claims that involved a bodily injury or property damage claim 

with a positive dollar value and did not involve uninsured or underinsured motorist 

claims, in the prior three years. This variable takes the values “0,” “1,” “2,” and “3 or 

more.” 

CLUE Data – Prior Collision / Medical Payments / Personal Injury Claims 

The number of claims that had a collision, medical payments or personal injury 

claim with a positive dollar value, and did not have uninsured or underinsured motorist, 

bodily injury, or property damage claims, in the prior three years. This variable takes the 

values “0,” “1,” and “2 or more.” 

CLUE Data – Prior Comprehensive-Only Claims 

Number of claims involving only comprehensive coverage with a positive dollar 

value, in the three prior years. This variable takes the values “0,” “1,” “2,” and “3 or 

more.” 

CLUE Data – Prior Towing and Labor-Only Claims 

Number of claims involving only towing and labor with a positive dollar value, in 

 

177 Because we received prior-claims data only for people who had the same address in the company data 

and in the CLUE data, the prior claims data used in the FTC’s analysis may be more limited than that used 

by companies when they underwrite and rate policies. Companies can ask applicants for prior addresses, 

and submit those addresses to be matched, as well. 
19

the prior three years. This variable takes the values “0,” “1,” “2,” and “3 or more.” 

CLUE Data – Prior Rental Reimbursement Claims 

Number of claims involving rental reimbursement, in the prior three years. This 

variable takes the values “0,” “1,” and “2 or more.” 

Number of Accidents

Number of accidents indicates the number of “chargeable” accidents that occurred 

prior to the beginning of the policy period. 178 This variable came from the companies, 

and may only reflect claims made policies at that company. The variable is missing for a 

large portion of the sample. The definition of “chargeable accident” may vary by 

company and by state, but is usually based on a dollar threshold and often on whether the 

driver was found to be at fault. For an accident to be considered chargeable, it must 

typically have occurred in the previous three years. This variable takes the values of 

“zero,” “one or more,” and “unknown.” 

Number of Violations 

The number of violations indicates the sum of major and minor moving violations 

for the driver assigned to a car that occurred prior to the beginning of the policy period. 

The definition of major violation may vary by company and by state. Typically, this 

variable only includes major and minor violations in the past three years. This variable 

takes the values “zero,” “one or more,” and “unknown.” 

Tenure 

Tenure is the number of years the customer had been with the company. Each 

year of tenure is a separate category for years 1 through 14, and then years of tenure are 

 

178 This variable may in many or all cases exclude accidents that occurred while the consumer was a 

customer of a different firm, which is one reason the CLUE data provides important additional information. 
20

combined into categories for “15 or 16 years,” “17, 18, or 19 years,” and a final category 

for tenures of 20 years or more. 

Property Damage Liability Limits 

This is the maximum amount customers would be reimbursed for a property 

damage liability claim. It was used only in liability regressions. Property Damage 

liability limits takes the values “$5,000 - $10,000,” “$15,000 - $20,000,” “$25,000 - 

$45,000,” “$50,000 - $80,000,” “$100,000 - $200,000,” “$250,000 - $325,000,” 

“$500,000 - $2,000,000,” and “missing or zero.” (Note that when these ranges are noncontiguous there were no policies in the database with values between the ranges.) 

Bodily Injury Liability Limits 

Bodily injury limit is the maximum amount customers would be reimbursed for 

bodily injury claims. There are two limits on bodily injury liability, the per-person limit 

and the total cost limit per occurrence. The two limits are highly correlated, so we based 

our bodily injury liability limit variable on the per-person limit. It was used only in 

liability regressions. It takes the values “$10,000,” “$12,500 - $15,000,” “$20,000,” 

“$25,000 - $40,000,” “$50,000 - $75,000,” “$100,000 - $150,000,” “$200,000 - 

$250,000,” “$300,000 - $400,000,” “$500,000 - $2,000,000,” and “missing or zero.” 

(Note that when these ranges are non-contiguous there were no policies in the database 

with values between the ranges.) 

State Minimums 

State minimums indicated whether the policy had only the minimum liability 

coverage required by law. It takes the values of “yes” and “no”. The FTC created this 

variable by comparing the liability limit variables with data on state legal minimum 
21

liability requirements. Information on state minimums, as of 2000, came from the NAIC 

2001/2002 Auto Insurance Database Report.179

Collision Deductible 

This is the deductible for collision claims. It takes the values “$0 - $50,” “$100 - 

$150,” “$200,” “$250 - $400,” “$500,” “$1,000 - $1,500,” and “missing.” This variable 

was used only in collision regressions. (Note that when these ranges are non-contiguous 

there were no policies in the database with values between the ranges.) 

Comprehensive Deductible 

This is the deductible for comprehensive claims. It takes the values “$0 - $25,” 

“$50,” “$100 - $150,” “$200,” “$250 - $300,” “$400 - $750,” “$1,000 - $5,000,” and 

“missing.” This variable was used only in comprehensive coverage regressions. (Note 

that when these ranges are non-contiguous there were no policies in the database with 

values between the ranges.) 

Annual Mileage 

Estimated annual mileage as reported by the customer. It takes the values of 

“7,500 miles or less,” “more than 7,500 miles,” and “unknown.” 

Principal / Occasional Driver 

Principal or occasional operator identifies whether the driver assigned to a vehicle 

was the primary user of the vehicle, or only used it occasionally. It is an indicator that is 

typically used only for young drivers. The variable had categories for “principal 

(driver),” “occasional (driver),” and “unknown”. 

 

179 National Association of Insurance Commissioners, “Auto Insurance Database Report 2001/2002” 

(2004). 
22

Use 

Vehicle usage reflects whether the vehicle was used primarily for “pleasure,” 

“farm,” “business,” “travel to work,” “all other uses,” or whether the use was “unknown.” 

Homeowner 

Indicates whether the customer owned a home. It takes the values “yes” and 

“no.” 

Multi-line Discount 

Multi-line discount designates whether a customer had multiple types of insurance 

with their auto carrier. The discount is commonly applied when a customer purchases 

homeowners insurance from the same company. Multi-line discount takes the values of 

“yes,” “no,” and “unknown.” 

Multi-Car 

Multi-car indicates whether there were multiple cars in the household covered by 

the same insurer. It takes the values “yes,” “no,” and “unknown.” 

State 

State where the vehicle was principally garaged. 

Model Year 

Model year of the vehicle. Each model year is a separate category, except the 

following groups of years: “2001 – 2002,” “1981 – 1984,” and “1980 or older.” 

Body Type 

Data from Edmund’s on the vehicle type. Body type takes the values 

“convertible,” “coupe,” “extended or crew cab pickup,” “regular cab pickup,” “four-door 

SUV,” “two-door SUV,” “hatchback,” “passenger minivan,” “wagon,” “sedan,” and 
23

“unknown.” 

Restraint System 

Data from Edmund’s on airbags and seat belts. Restraint system takes the values 

“only passive seatbelts,” “only active seatbelts,” “seatbelts and driver’s front airbag,” 

“seatbelts and driver and passenger front airbags,” “more than seatbelts and front 

airbags,” and “unknown.” 

Displacement 

Data from Edmund’s on the size of the engine in the vehicle. Engine 

displacement is an indicator of the power of the engine. It takes the values “less than 2.7 

liters,” “2.7 – 4.3 liters,” “More than 4.3 liters,” and “unknown.” 

APPENDIX E 

THE SCORE BUILDING PROCEDURES 

1

APPENDIX E. Score Building Procedures

 E. 1. Developing the FTC Base Model

The FTC credit-based insurance score-building methodology produces “pure 

premium” scoring models. That is, the models are developed to predict total dollars paid 

out on claims on a policy in a year.180 To have a single scoring model that predicts losses 

for any of the four major coverages, we combined total claims across coverages into a 

single measure of losses.181

The steps for building a credit-based insurance scoring model are first described, 

and then the logic underlying the procedure is discussed. 

• An ordinary least squares model (“OLS model”) is run using total dollars of 

claims as the dependent variable, and the 180 credit history variables as the 

explanatory variables. 182 The results of the OLS model are used to generate a 

“proto-score.” 

• A Tweedie GLM model is run, using total dollars of claims as the dependent 

variable, and all the standard risk variables and the proto-score as the explanatory 

variables.183 Predicted total dollars of claims are calculated for each record using 

the results of the Tweedie GLM model.184 An “adjusted claims” variable is 

calculated by dividing actual total dollars of claims by predicted total dollars of 

claims. 

• Each credit history variable is then divided into optimal “bins.” This is done 

using an approach developed by staff of the FRB. The relationship between each 

credit history variable and adjusted claims is evaluated separately. First, the 

 

180 Because many of the records are for less than a full year, total dollars of claims are adjusted for the 

period of time each car was actually covered by one of the companies in the sample. 

181 Claims on first party medical coverages – MedPay and personal injury protection – are also included in 

the “total losses” variable. 

182 The credit history variables were first converted from continuous variables into discrete variables. This 

was done using a simple rule of thumb of dividing the values into “bins” that each contains at least roughly 

10% of the sample. (So, if 50% of the sample had a value of zero for a given variable, there would be one 

category for “zero,” and up to five additional bins.) 183 Because we are combining claims from across coverages, we also include dummy variables indicating 

whether the policy included collision, comprehensive, MedPay, and/or personal injury protection coverage. 184 The “proto-score” is used in the model estimation as a control, but is not used when the predicted pure 

premium is calculated. The use of a “proto-score” in this way follows a suggestion from several score 

builders at firms. It is done simply to minimize the effect of other variables that are correlated with score, 

such as age, picking up variation that would be attributed to score if score were included in the model. 
2

credit history variable is divided into the two categories that create the biggest 

difference in mean adjusted claims between the two categories. These categories 

are then divided into additional categories, until the point where further divisions 

would not lead to statistically significant differences in mean adjusted claims 

across new categories.185

• A forward-selection OLS model is run, with adjusted claims as the dependent 

variable, and the binned credit history variables as the candidate explanatory 

variables. The process works by first choosing the variable that, on its own, is 

most predictive of risk, based on an F-test. The next variable chosen is the 

variable that adds the most predictive value when used in a model with the first 

variable chosen (again, based on an F-test). This process continues, with credit 

history variables being added, one by one, until a pre-determined threshold is 

reached. 

• A Tweedie GLM model is run with actual total dollars of claims as the dependent 

variable, and the standard risk variables and the “winning” credit history variables 

as the explanatory variables. 

• The coefficients on the credit history variables from the Tweedie GLM estimated 

in the previous step are used to generate a scorecard for the “FTC credit-based 

insurance scoring model.” 

The underlying logic of this procedure is that we are attempting to find the set of 

credit history variables that best predict total dollars of claims, after controlling for noncredit risk variables. The non-credit risk variables are initially included in the model by 

adjusting total dollars of claims by a measure of risk based on these variables. Steps one 

and two do this. The third step, the binning of the credit-history variables, is done for 

two reasons. (The alternative would be to keep the credit-history variables as continuous 

variables.) Dividing the values into bins is a simple way of allowing the effects of the 

variables to vary in complex non-linear ways over the range of values. Using bins also 

 

185 Two restrictions are placed on the binning process. The first is that no bin could be less than 1⁄2% of the 

total sample. This is done to avoid “over fitting” the data, and to avoid convergence problems when binned 

data are used in the Tweedie GLM stage. The binning procedure was also run using either a monotonicity 

requirement, meaning that average claims must either rise or fall across the range of bins, or a “singleturning” requirement, meaning that if average adjusted claims were not monotonic, they could first go up 

and then down, or vice-versa, but not go up-down-up or down-up-down, etc. Both restrictions led to the 

same set of optimal bins. 
3

makes the scorecard – the tool for actually calculating a score – much simpler than would 

other ways of allowing non-linear effects. 

The fourth and fifth steps are the core of the score-building process. First, the 

most predictive credit-history variables are determined by the forward-selection 

procedure. The forward-selection procedure runs a separate OLS model regression, with 

adjusted claims as the dependent variable, for every credit-history variable (i.e., 180 

separate regressions). It then determines which credit-history variable provides the most 

predictive power. It then runs through that same process, and chooses the variable that 

adds the most predictive power to a model that includes the “winning” variable from the 

first step. This process continues, adding variables one-by-one, until it hits some 

stopping rule. 186 We used two stopping rules. The first was that if the estimated effect on 

adjusted claims of the next potential variable was not statistically significantly different 

from zero (“no effect”) at the 10% level, the procedure stopped. This approach tended to 

produce a model with a very small number of variables, fewer than ten. We also used an 

alternative where the procedure continued until it had selected the first fifteen “winning” 

variables. Fifteen was chosen arbitrarily, based on scorecards we reviewed and 

discussions with professional score builders and staff at the Federal Reserve Board. 

The final step in the score building process is calculating the scorecard. This is 

done by estimating a Tweedie GLM with actual total claims, instead of adjusted total 

claims, as the dependent variable. All of the non-credit risk variables are included in the 

 

186 Ideally, the forward selection procedure would be run using a Tweedie GLM model, as that is the 

preferred way of modeling total dollars of insurance claims. Maximum likelihood procedures are apt to 

“crash,” however, especially when run on data with many highly-correlated variables, like credit history 

data. (It is common in the industry to use some form of OLS-based variable selection procedure.) Our 

approach is a compromise. We use the OLS model for the forward-selection procedure, which determines 

the “winning” variables, but estimate the final scoring model using a Tweedie GLM model of the actual 

pure premiums with all of the non-credit variables. 
4

model, along with the “winning” credit history variables. The scorecard is made up of 

the estimated coefficients on the credit history variables. The scorecards we report show 

the inverse of the exponentiated coefficients. A score is calculated by multiplying 

together the coefficients for each credit history variable, and this produces the inverse of 

the predicted relative risk. The coefficients must be exponentiated because the Tweedie 

GLM has a log-linear functional form. We use the inverse of the coefficients so that a 

higher score will be associated with a lower predicted risk. 

 E.2 Developing “Race Neutral” Models

The FTC used two approaches to controlling for race, ethnicity, and income in the 

score-building process. One approach was to include controls for race, ethnicity, and 

income in the forward-selection step, when the “winning” credit history variables were 

chosen.187 This means that the variables were not chosen because of a correlation with 

race, ethnicity, or income. Race, ethnicity, and income controls were also included when 

the final Tweedie GLM was run to generate the scorecard. So, any relationship between 

risk and race, ethnicity, and income was controlled for, and would not be picked up by 

the weights on the credit history variables. (Note that while race, ethnicity, and income 

are included in the model that determines the scorecard, they are not themselves used to 

calculate a score.) 

The other approach was to build the model using only non-Hispanic whites. This 

 

187 An alternative approach we used was to include race, ethnicity, and income controls in the step of the 

model-building process when the “adjusted pure premium” is calculated. The adjusted pure premium was 

therefore adjusted for those variables. The binning of the credit history variables was therefore done in a 

way that was purged of any relationship between race, ethnicity, and income, and claims. In addition, the 

forward-selection process was done with the race adjusted pure premium as the dependent variable. So, the 

credit history variables were chosen for the model using a dependent variable that was adjusted for race, 

ethnicity, and income. This approach gave very similar results to results of the model discussed in the body 

of the report. 
5

was done by limiting the development sample to people who answered “White, NonHispanic” to the post-1981 SSA questionnaire and the records that represent the “nonHispanic white” imputed probabilities of people for whom we only have pre-1981 SSA 

data (which include the weight from the imputation process). 

 E.3 Discounting Variables for Differences across Racial and Ethnic Groups

To force the model-building procedure to produce models with smaller 

differences across racial and ethnic groups, we modified the forward selection step to 

take those differences into account. Normally, the forward selection step runs a series of 

OLS regression models, with adjusted total claims as the dependent variable and credit 

history variables as the explanatory variables. One regression is run for each credit 

history variable. The credit history variable with the largest impact on predicted risk at 

each step, as measured by an F-test, is added to the set of “winning” variables. 

This step was modified by also running an OLS regression for each credit history 

variable with race and ethnicity as the dependent variable. Race and ethnicity was 

captured using indicator variable for whether the individual was non-Hispanic white or 

minority (i.e., all minority groups were combined into one category, to simplify the 

modeling). The R2

 statistics were then calculated for the risk OLS model and the 

race/ethnicity OLS model, and used jointly to choose winning variables. The R2

 statistic 

from the risk equation is a measure of how much power the credit history variable has to 

predict risk. The R2 statistic from the race and ethnicity model is a measure of how much 

the credit history variable differs by race and ethnicity. We used these two measures to 

choose variables for the model in a variety of ways. The approach described in the body 

of the report was to first normalize the R2

 statistics within each set of regressions – the 
6

risk regressions or the race and ethnicity regressions – by dividing the R2 for the 

regression for each variable by the largest R2

 in that set of regressions. That is, the R2

statistics from the risk regressions for each credit history variable were divided by the 

largest R2 from all of the risk regressions, and similarly for the race and ethnicity 

regressions. We then compared the normalized R2

 statistics to select the variables to 

include in the model.188 

 

188 The model described in the body of the report was determined by subtracting twice the normalized R2

 of 

the race and ethnicity regression for each variable from the normalized R2

 of the risk regression for that 

variable. At each step, we chose the variable with the largest difference as the winning variable. Taking 

the difference between the normalized R2

 statistics, without doubling the normalized R2 from the 

race/ethnicity regression, resulted in a model with much larger differences across racial and ethnic groups. 

Using the ratio of the R2

 statistics from the two regressions resulted in a model that was very similar to that 

discussed in the body of the report. 
APPENDIX F 

ROBUSTNESS CHECKS AND LIMITATIONS OF THE ANALYSIS 

1

APPENDIX F. Robustness Checks and Limitations of the Analysis

The FTC conducted numerous additional analyses to confirm the results presented 

in the body of the report, and to test whether those results are robust to the credit score 

used, the database used, the use of a sub-sample, and a variety of modeling decisions. 

There remain several limitations of the database and the analysis that could not be fully 

addressed through these robustness checks. 

F.1 Limitations of the Data and the Analysis

No Information on People who did not Obtain Insurance 

The FTC did not have information on insurance applicants who were denied 

coverage by the firms that provided data. We could therefore not directly evaluate the 

impacts of credit-based insurance scores on consumers’ ability to obtain insurance from a 

given firm. However, the analysis of state residual markets data in NAIC reports shows 

that scores do not appear to have adversely affected consumers’ ability to obtain 

insurance through the normal, voluntary market for automobile insurance. 

Single National Model 

Underwriting and rating plans are determined by firms below the national level, 

and often at the state level. The FTC’s analysis includes controls for state, but does not 

separately model risk by state. The results of our national model may differ from the 

results of separate state-level models, especially if the effects of particular risk variables 

differ across states. 

Pooled Company Data 

The FTC risk models were estimated using pooled data from multiple firms. 
2

Individual firms estimate the risk posed by their customers, and the results of models 

estimated using data from a single company may differ from those of a model estimated 

using pooled data. 

Sub-Sample of Industry 

The FTC database includes data from five firms that together represented over 1⁄4 

of the entire automobile insurance market as of 2000. Despite having data from a fairly 

large share of the market, we know that this sample likely under-represents the highestrisk segment of the market. (An analysis that focuses on a sub-sample of the riskiest 

policies in our database is presented in section F.2, below.) In addition, there may be 

other ways in which these firms differ from the market, as a whole. 

Territorial Risk Variable 

The territorial risk variable in the FTC database is based on ZIP-code average 

property damage liability claims. It is a powerful predictor of risk for property damage 

liability, bodily injury liability, and collision coverages, but it may differ from the 

territorial risk measures used by individual firms. More importantly, this territorial risk 

variable is not a powerful predictor of risk for comprehensive coverage. As discussed in 

the text, this is likely to lead to over-estimates of the relationship of both score and 

demographic characteristics like race, ethnicity, and neighborhood income to 

comprehensive coverage risk. 

F.2 Robustness Checks

FICO Score 

The credit-based insurance score results reported in the body of the report are for 

the ChoicePoint Attract Standard Auto score. All of the analyses were also run using the 
3

FICO “Standard Auto, Greater than Minimum Limits” credit-based insurance score. The 

results were similar, both qualitatively and quantitatively, to the results for the 

ChoicePoint score. 

No Nationally Representative Weights 

The level of racial, ethnic, and income diversity of the sample could affect the 

results of the “proxy” analysis. The analysis in the body of the report was done using a 

sample weighted to match the racial, ethnic, and neighborhood income distribution of the 

national population of car owners. While this seems a reasonable approach, that 

population may have a different racial, ethnic, or income mix than the national population 

of car insurance customers, or the mix of the pool of customers of any individual firm. 

We also did the analysis without using the tract and race weights that make the sample 

nationally representative. The results were qualitatively very similar to the results from 

using the weights. The impact of scores on the estimated risk of African Americans and 

Hispanics was slightly larger, with the impact on African Americans being an average 

increase of 11.6% (versus 10.0% with weights) and for Hispanics 5.8% (versus 4.2% 

with weights). The estimated proxy effect was very similar. 

Outliers 

We suspected that policies with more than six claims on a coverage may have 

reflected data errors, so those policies were dropped from the analysis reported in the 

body of the report. Leaving those policies in did not affect the results of the analysis. 

The use of nationally representative weights resulted in several claims becoming 

outliers, despite the capping of those weights at the 99.95th percentile. There were four 

people with large claims and small earned car years who lived on Census tracts that were 
4

highly under-represented in the database whose claims became outliers when the Census 

tract weights were applied. Two of these had no impact on any results. These were a 

collision claim paid to a Hispanic consumer in the lowest score decile, and a 

comprehensive claim paid to an African American consumer in the 3rd-lowest score 

decile. There were two outliers that did have a small impact on the results described in 

the body of the report. There was one bodily injury liability claim, filed by a nonHispanic white consumer in the second score decile (the second from the bottom) that 

became an outlier. Capping the weighted value of the claim at the size of the next-largest 

weighted claim reduced the estimated risk effect of the second decile in the bodily injury 

liability model by several percentage points. This did not affect any other results of the 

analysis. There was one comprehensive claim, filed by an African American consumer 

in the 9th score decile (second from the top) that became an outlier when the nationally 

representative weight was applied. This did not affect the estimated risk effect for the 9th

decile in the overall comprehensive claims model, and therefore does not affect any of 

the overall results of the analysis. It did have a large effect on the estimated risk effect of 

the 9th decile for African Americans when race and ethnicity were interacted with score 

deciles (this is shown in Figure 14). Capping this claim brought that estimated risk effect 

down somewhat, but only when the observation was dropped did the estimate fall in line 

with the surrounding deciles. In any case, the estimated risk for the 9th decile for African 

Americans was not statistically significantly different from that of the overall sample, 

even when the outlier was not capped. 

Full-Sample Models 

With the exception of the analysis of the CLUE claims data, the results in the 
5

body of the report are based on a sub-sample of records. Much of the analysis required 

the SSA race ethnicity data, and therefore could be done only with the sub-sample for 

which we obtained those data. We also estimated the basic risk models, without 

race/ethnicity/income controls, on the complete sample. The results were very similar to 

the results from the sub-sample that are described in the body of the report. 

Census-Only Race and Ethnicity Data 

In the body of the report, we combined data on race and ethnicity from three 

sources: Social Security Administration records, a Hispanic Surname match, and Census 

data. We also estimated models using only Census race and ethnicity data, measured at 

the Census block level. This resulted in a weaker relationship between race/ethnicity and 

claims risk, which, in turn, resulted in a smaller estimated “proxy effect.” These results 

are what would be expected when race and ethnicity are measured less precisely. 

Absolute Income Measure 

The results presented in the body of the report that relate to income are based on 

assigning people to one of three income categories based on the median income of the 

Census tract where the person lived relative to the median income in their MSA. To 

determine whether using relative income instead of absolute income affects the results of 

our analysis, we re-ran the analysis using three categories based on tract median income, 

not relative to the MSA median. This did not affect the results of the analysis. 

Race and Ethnicity Imputation Cut-Offs 

As discussed in Appendix C, when multiple data sources were used to impute the 

race and ethnicity of people for whom we only had a pre-1981 SSA race/ethnicity 

answer, we imposed a minimum cut-off on the predicted likelihood that someone was of 
6

a given race or ethnicity. When the estimated likelihood of being of a particular race or 

ethnicity was very low, we set the probability to zero. To test whether this decision 

affected the results, we re-ran the analysis without using the cut-off. This did not affect 

the results. 

High-Risk Sub-Group 

Because of the way the sample was drawn by the companies, the FTC database 

probably under-represents the highest-risk portion of the automobile insurance market. 

In an attempt to determine whether our analysis would extend to that portion of the 

market, we estimated risk models limited to the riskiest people in our database, as 

determined by non-credit factors. To do this, we first ran a risk model without credit 

score, on the full model sample, that combined claims from the four major coverages. 

We then predicted each individual’s expected total claims (their risk), and created a subsample consisting of the 20% of the sample with the highest predicted total dollars of 

claims. We then ran risk models for each of the four major coverages that included credit 

scores on the “risky” sub-sample. The estimated relationships between risk and score for 

the sub-sample were similar to the relationships in the overall sample. 

Estimating Total Losses by Modeling Frequency and Severity Separately 

Most of the results in the report are from Tweedie GLMs of total dollars of 

claims. In addition, we modeled total dollars of claims by separately modeling frequency 

of claims, using Poisson regressions, and severity of claims, using Gamma GLMs, and 

then combined the estimates from the two models. The estimated relationships between 

score and risk from combining the results from these two models were essentially 

identical to the results from the single-step model. 
7

Single Combined-Coverage Model 

In the body of the report, we present results from analyzing each type of 

automobile coverage separately. In addition to the separate models by coverage, we 

estimated a combined-risk model for the four major coverages. This was done by 

summing claims on the four major coverages into a single claims variable. Indicator 

variables were included to control for differences in the set of coverages purchased by 

consumers. Scores were predictive of risk in this combined-coverage model, and the 

effects of scores on the predicted risk of different racial and ethnic groups from the 

combined-risk model were very similar to the results from combining the results from the 

separate coverage models. The overall “proxy” results for scores were also similar to the 

results from combining the results from the separate coverage models. 

“Tiering” 

The risk models used in the body of the report are single-equation models, where 

all risk factors enter into the single equation. Some firms use credit-based insurance 

scores to determine the risk category in which a customer is placed. This may allow the 

effects of non-credit risk variables to vary depending on a person’s score (essentially 

interacting score with other risk variables). To determine whether this would affect the 

results of our analysis, we divided the sample into three groups based on score. We then 

ran separate risk models for the three groups, with and without score, and measured the 

impact on predicted risk for different racial and ethnic groups. The results were very 

similar to the single-model approach used in the body of the report. 

Number of Score Categories 

We use score deciles throughout the report. To test whether the choice of deciles 
8

was important to the results, we re-ran the analysis using 20 categories of scores 

(“ventiles”). The results for predicted risk, predicted impacts on minorities, and the 

results relating to “proxy effects” from using ventiles was very similar to the results from 

using deciles. 

Number of Geographic Risk Categories 

In the results reported in the body of the report, we use controls for geographic 

risk that assign people to five categories (“quintiles”). To test whether the choice of 

quintiles was important to the results, we re-ran the analysis using ten categories of 

geographic risk (deciles). Using deciles of geographic risk, instead of quintiles, did not 

affect the results. 
APPENDICES

TABLES

Census 

Unweighted

With

Tract Weights

With

Tract and Race 

Weights

(a) (b) (c) (d)

Race

African Americans 8.4% 4.3% 6.0% 8.4%

Hispanics 7.8% 2.8% 3.7% 7.8%

Asians 3.1% 3.1% 2.9% 3.1%

Non-Hispanic Whites 80.8% 89.8% 87.5% 80.8%

Income

Low 18.2% 12.3% 17.6% 19.2%

Medium 50.6% 44.0% 51.0% 50.3%

High 31.3% 43.7% 31.4% 30.5%

Notes:

1) Percentages are relative to the group of consumers included in these calculations.

FTC Database

2) The tract weights were calculated using the ratio of the share of vehicles in the 2000 Census in each tract divided by 

the share of vehicles in the FTC database in each tract. The subsequent race weights are simply the ratio of the share of 

each race group in the Census data over the share of each race group in the FTC database, after applying the tract 

weights. See Appendix C for details on the development of the weights.

3) The final proportions differ slightly from those reported in the table on the sub-sample used for model estimation and 

analysis because that sample has several additional minor restrictions that were not applied to the sample used to 

develop the weights.

TABLE A1.

Development of Nationally Representative Weights:

Share of Vehicles by Race, Ethnicity and Neighborhood Income
Model Sub-Sample

With Nationally

Full Sample Model Sub-Sample Represenative Weights

(a) (b) (c)

(Median or Percent) (Median or Percent) (Median or Percent)

Gender

Male 29.8% 29.2% 25.8%

Female 31.4% 32.1% 28.9%

Unknown 38.8% 38.7% 45.3%

Marital Status

Single 12.3% 13.1% 12.3%

Married 31.6% 33.1% 27.4%

Divorced / Widowed 2.4% 2.6% 2.9%

Unknown 53.7% 51.1% 57.5%

Accidents

Zero 56.8% 59.7% 60.7%

One or More 4.5% 4.9% 4.7%

Unknown 38.6% 35.4% 34.6%

Miles Driven

<7500 22.1% 22.0% 22.5%

>7500 50.4% 50.6% 55.0%

Unknown 27.6% 27.5% 22.5%

Multi-Line Discount

Yes 34.5% 34.1% 36.6%

No 35.3% 34.8% 40.4%

Unknown 30.3% 31.1% 23.1%

Principal Operator 

Yes 28.2% 27.8% 27.0%

No 6.0% 5.7% 5.9%

Unknown 65.8% 66.5% 67.1%

Use

Business 0.7% 0.6% 0.6%

Farm 0.8% 0.6% 1.0%

Pleasure 42.3% 43.1% 44.0%

Work 15.7% 16.9% 18.5%

All Other 12.8% 9.6% 11.2%

Unknown 27.8% 29.2% 24.8%

Homeowner

Yes 55.6% 56.3% 52.5%

No 44.4% 43.7% 47.5%

Multiple Cars

Yes 61.8% 60.1% 53.0%

No 14.5% 14.8% 14.9%

Unknown 23.7% 25.1% 32.0%

(continued. . .)

TABLE A2.

Summary Statistics for the Full FTC Database and the Sub-Sample Used for 

Model Estimation and Analysis
Model Sub-Sample

With Nationally

Full Sample Model Sub-Sample Represenative Weights

(a) (b) (c)

(Median or Percent) (Median or Percent) (Median or Percent)

Major Violations

Positive 0.3% 0.3% 0.4%

Zero 64.6% 64.9% 59.5%

Unknown 35.1% 34.8% 40.1%

Minor Violations

Positive 5.1% 5.5% 5.1%

Zero 54.3% 53.9% 47.2%

Unknown 40.6% 40.6% 47.8%

Vehicle Body Type

Convertible 1.6% 1.6% 1.2%

Coupe 5.5% 5.8% 6.2%

Ext/Crew cab pickup 4.4% 4.4% 4.8%

Four-door SUV 9.7% 9.8% 8.5%

Hatchback 3.7% 3.8% 3.7%

Passenger MiniVan 5.5% 5.7% 5.5%

Regular Cab Pickup 3.6% 3.5% 4.5%

Two-door SUV 1.8% 1.8% 1.8%

Wagon 2.9% 3.0% 2.1%

Sedan 31.0% 31.5% 30.2%

Unknown 30.4% 29.0% 31.5%

Restraint System

Driver's front airbags 10.7% 10.9% 10.8%

Driver/Psgr front airbags 36.3% 37.4% 35.5%

Just active belts 12.1% 12.0% 12.5%

Just passive belts 5.6% 5.7% 6.1%

More than front airbags 5.0% 5.0% 3.5%

Unknown 30.4% 29.0% 31.5%

Prior Claim†

Under & Uninsured Motorist 1.6% 1.7% 2.0%

BI & PD 14.4% 15.1% 14.4%

Coll., Med, & PIP 12.9% 13.9% 13.5%

Comprehensive 19.3% 20.6% 19.8%

Towing and Labor 6.7% 7.2% 8.1%

Rental Reimbursement 7.3% 8.1% 8.4%

None of the above 60.9% 58.3% 58.9%

Age 47 46 46

Share Unknown 12.6% 12.3% 11.7%

Tenure 10 10 8

Share Unknown 11.7% 11.3% 12.8%

(continued. . .)

TABLE A2.

Summary Statistics for the Full FTC Database and the Sub-Sample Used for 

Model Estimation and Analysis (Continued)
Model Sub-Sample

With Nationally

Full Sample Model Sub-Sample Represenative Weights

(a) (b) (c)

(Median or Percent) (Median or Percent) (Median or Percent)

Property Damage Liability Limit $50,000 $50,000 $50,000

Share Unknown 3.2% 3.1% 2.0%

Bodily Injury Liability Limit $100,000 $100,000 $100,000

Share Unknown 3.6% 3.4% 2.3%

Collision Deductible $500 $500 $300

Share Unknown 0.0% 0.0% 0.0%

Comprehensive Deductible $200 $200 $100

Share Unknown 0.0% 0.0% 0.0%

Model Year 1994 1994 1994

Share Unknown 0.8% 0.4% 0.3%

Coverage Combinations

All Four Main Coverages 77.3% 82.6% 80.6%

Liability and Comprehensive 13.3% 13.3% 15.4%

Liability Only 4.1% 4.1% 4.1%

Other Coverage Combinations 5.4% 0.0% 0.0%

Race/Ethnicity

African American NA 4.3% 8.4%

Hispanic NA 2.8% 7.7%

Asian NA 3.1% 3.1%

Non-Hispanic White NA 89.9% 80.8%

Number of Policies 1,434,041 275,509 275,509 

Number of Vehicles 2,284,330 458,940 458,940 

Total Car Years 1,808,584 399,100 399,100 

Note: See Appendix C for details on the data sources and the construction of the database. See Appendix D for a discussion of

how the sub-sample used for model estimation and analysis was chosen.

TABLE A2.

Summary Statistics for the Full FTC Database and the Sub-Sample Used for 

Model Estimation and Analysis (Continued)

†: Some Prior Claims categories are not mutually exclusive, therefore the shares can add up to more than 100%