DRAFT 

May 2015 

Data Point: 

Credit Invisibles 

The CFPB Office of Research
2 CFPB DATA POINT: CREDIT INVISIBLES 

 Kenneth P. Brevoort 

 Philipp Grimm 

 Michelle Kambara 

This is another in an occasional series of publications from the Consumer Financial Protection 

Bureau’s Office of Research. These publications are intended to further the Bureau’s objective of 

providing an evidence-based perspective on consumer financial markets, consumer behavior, 

and regulations to inform the public discourse. 
3 CFPB DATA POINT: CREDIT INVISIBLES 

Table of Contents 

Table of Contents ........................................................................................................ 3

1. Introduction ........................................................................................................... 4

2. Data ........................................................................................................................ 7

2.1 Data Sources ............................................................................................. 7

2.2 Dataset Creation ....................................................................................... 9

3. How Many Americans Have Limited Credit Histories? .................................... 12

4. Patterns of Limited Credit History by Race and Ethnicity .............................. 16

4.1 Patterns by Race or Ethnicity ................................................................. 16

4.2 Racial or Ethnic Patterns by Age ............................................................ 18

5. Conclusions ........................................................................................................ 24

References ................................................................................................................. 26

Appendix A: ............................................................................................................... 28

Effect of Fragment File Exclusions ................................................................. 28

Appendix B: ............................................................................................................... 31

Data Used in Figures ....................................................................................... 31

 
4 CFPB DATA POINT: CREDIT INVISIBLES 

1. Introduction 

Consumers with limited credit histories reflected in the credit records maintained by the three 

nationwide credit reporting agencies (NCRAs) face significant challenges in accessing most 

credit markets.1

 NCRA records are often used by lenders when making credit decisions. In 

particular, lenders often use credit scores, such as one of the FICO or VantageScore scores, that 

are derived entirely from NCRA records when deciding whether to approve a loan application or 

in setting a loan’s interest rate. If a consumer does not have a credit record with one of the 

NCRAs or if the record contains insufficient information to assess her creditworthiness, lenders 

are much less likely to extend credit. As a result, consumers with limited credit histories can 

face substantially reduced access to credit. 

In broad terms, consumers with limited credit histories can be placed into two groups. The first 

group is comprised of consumers without NCRA credit records. We refer to this group as “credit 

invisibles.” The second group includes consumers who, while they have NCRA credit records, 

have records that are considered “unscorable,” meaning they contain insufficient credit histories 

to generate a credit score. Generally speaking, a credit record may be considered unscorable for 

two reasons: (1) it contains insufficient information to generate a score, meaning the record 

either has too few accounts or has accounts that are too new to contain sufficient payment 

history to calculate a reliable credit score; or (2) it has become “stale” in that it contains no 

recently reported activity. The exact definition of what constitutes “insufficient” or “stale” 

information differs across credit scoring models, as each model uses its own proprietary 

definition. Our analysis is based on a commercially-available credit scoring model that we 

believe uses a relatively narrow definition of a “scorable” credit record, but one that we believe is 

consistent with most credit scores used today. We refer to these records as “unscored” rather 

 

1 The three NCRAs are Equifax, Experian, and TransUnion. 
5 CFPB DATA POINT: CREDIT INVISIBLES 

than “unscorable” to reflect the fact that other credit scoring models might generate scores for 

these records. Nevertheless, we believe our estimates of the population with unscored credit 

records accurately reflect the circumstances faced by consumers with limited credit histories. 

The challenges that credit invisibles and consumers with unscored records face in accessing 

credit markets has generated considerable attention from researchers and industry participants. 

Several studies have explored the potential of various types of “alternative data” to supplement 

the information contained in the NCRA credit records and allow credit scores to be generated 

for these consumers.2 Stakeholders have debated the implications of doing so for those with 

limited credit history as well as those with scorable files whose credit profiles might change with 

the addition of such data. Several industry participants have also developed scoring products 

that are aimed specifically at these populations.3

Despite all of this attention, very little is known about the number or characteristics of credit 

invisibles or consumers with unscored credit records. This Data Point documents the results of 

a research project undertaken by Staff in the Office of Research of the Consumer Financial 

Protection Bureau (CFPB) to better understand how many consumers are either credit invisible 

or have unscored credit records and what the demographic characteristics of such consumers 

are. 

This analysis was conducted using the CFPB’s Consumer Credit Panel (CCP), a 1-in-48 

longitudinal sample of de-identified credit records purchased from one of the NCRAs and 

representative of the population of consumers with credit records. This dataset contains 

information on almost 5 million consumer credit records. While these data contain no directidentifying information (such as name, address, or Social Security Number), for each credit 

 

2 For example, see Turner, et al. (2006), Experian (2014), and Schneider and Schutte (2007) for utility payments, 

Experian RentBureau (2014) for rental payments, and CFPB (2014) for remittances. 

3 For example, FICO recently announced that it is launching a pilot project that extends the number of consumers 

whose records can be scored using alternative data on utility and telecommunication bill payments and property 

record data (FICO, 2015). LexisNexis has also introduced a credit scoring model, RiskView, that uses alternative 

data to expand the number of credit records that can be scored (Feinstein, 2013). The new version of the 

VantageScore, version 3.0, uses alternative data when it is available on a credit record to expand the number of 

consumers whose records can be scored (VantageScore, 2013). For other examples, see Jacob and Schneider 

(2006). 
6 CFPB DATA POINT: CREDIT INVISIBLES 

record we observe the consumer’s census tract, year of birth, and a commercially-available 

credit score. 

We use these data from December 2010 to estimate the number of credit invisibles in each tract 

by taking the difference between the number of adults living in the tract according to the 2010 

Decennial Census and the number of credit records in each tract, as estimated from the CCP. 

Since the 2010 Census publishes data on population by age, and since the CCP contains year of 

birth, we estimate the number of credit invisibles in each tract for each of thirteen different age 

groups. For each of these age groups, we also calculate the number of consumers with unscored 

credit records in each tract using the CCP. Then using variation across census tracts in the 

racial and ethnic composition of the population and their household incomes, which we take 

from the 2008-2012 American Community Survey, we estimate how the incidence of being 

credit invisible or having an unscored credit record differs across these demographic 

characteristics. 

Key findings of this report include: 

• As of 2010, 26 million consumers in the United States were credit invisible, representing 

about 11 percent of the adult population. An additional 19 million consumers, or 8.3 

percent of the adult population, had credit records that were treated as unscorable by a 

commercially-available credit scoring model. These records were about evenly split 

between those that were unscored because of an insufficient credit history (9.9 million) 

and because of a lack of recent history (9.6 million). 

• There is a strong relationship between income and having a scored credit record. Almost 

30 percent of consumers in low-income neighborhoods are credit invisible and an 

additional 15 percent have unscored records. These percentages are notably lower in 

higher-income neighborhoods. For example, in upper-income neighborhoods, only 4 

percent of adults are credit invisible and another 5 percent have unscored credit records. 

• Blacks and Hispanics are more likely than Whites or Asians to be credit invisible or to 

have unscored credit records. About 15 percent of Blacks and Hispanics are credit 

invisible (compared to 9 percent of Whites and Asians) and an additional 13 percent of 

Blacks and 12 percent of Hispanics have unscored records (compared to 7 percent of 

Whites). These differences are observed across all age groups, suggesting that these 

differences materialize early in the adult lives of these consumers and persist thereafter. 
7 CFPB DATA POINT: CREDIT INVISIBLES 

2. Data 

2.1 Data Sources 

The data used in this study come from three sources. The first is the CFPB’s Consumer Credit 

Panel (CCP), a longitudinal sample of approximately 5 million de-identified credit records that 

is nationally representative of the credit records maintained by one of the NCRAs. This study 

primarily uses data from December 2010; however, as described below, we also use information 

for these same consumers from December 2014 in cleaning the data. 

For each time period, the entire credit record is supplied in the CCP, excluding any directidentifying personal information (such as name, address, or Social Security Number). In 

addition to the credit records, the CCP includes a commercially-available credit score, which we 

use to indicate which records were scored and which were not. For each unscored record, an 

“exclusion code” is provided indicating why the record could not be scored using the model for 

the commercially-available credit score. 

Like most credit scoring models, the model that generated the scores in the CCP was built to 

predict future credit performance (that is, the likelihood, relative to other borrowers, that a 

consumer will become 90 or more days past due on a credit obligation in the following two 

years).4 In some cases, the model builders will determine that a credit record does not contain 

enough information to make a suitably reliable prediction. During score development, these 

records are excluded and are unscored by the model going forward. 

 

4 This is a generic definition of “credit performance” used in credit scoring models. The exact definition used will vary 

from one credit scoring model to another. For more information on measures of performance in credit scoring 

models, see Board of Governors of the Federal Reserve System (2007). 
8 CFPB DATA POINT: CREDIT INVISIBLES 

There are two types of unscored records in the CCP.5 The first, “insufficient unscored” records, 

do not contain enough information to generate the score, meaning either that the record 

contained too few reported accounts or accounts that did not have a sufficiently long credit 

history. The second type, “stale unscored” records, do not contain any recently reported 

information. Our analysis examines these two types of unscored credit records separately. 

When available, a year of birth is included in the CCP for each record.6 We use this information 

to calculate the age of each consumer at the end of 2010. This allows us to examine how the 

incidence of being credit invisible or having an unscored credit record varies with age. Though 

credit records in the CCP do not include address information, each consumer’s census tract 

using 2010 census definitions is provided. This allows us to measure how credit records are 

distributed across the country. 

The second source of data used in this study is the 2010 Decennial Census, conducted by the 

U.S. Census Bureau. The Decennial Census indicates the number of consumers in each census 

tract. It also provides information on the racial and ethnic mix of each tract. In our analysis, we 

focus on four different racial or ethnic groups: Hispanics or Latinos (“Hispanics”), NonHispanic Asians (“Asians”), Non-Hispanic Blacks or African Americans (“Blacks”), and nonHispanic Whites (“Whites”). All other non-Hispanic racial groups, which include American 

Indians or Alaska Natives, Native Hawaiians or Other Pacific Islanders, or multi-racial 

individuals, are included in a category labelled “Other.” 

The third source of data comes from the 2008-2012 American Community Survey (ACS), which 

is also conducted by the U.S. Census Bureau. Among other information, the ACS includes the 

median household income in each tract, county, and Metropolitan Statistical Area (MSA). We 

use this information to calculate the “relative income” of each tract. Relative income is defined 

as the ratio between the median household income of the tract and the median household 

income of the surrounding area, which is the MSA for urban tracts or the county for rural tracts. 

Following the definitions used in the Community Reinvestment Act, we then characterize each 

 

5 Credit records in the CCP will also be unscored if the record belongs to a deceased consumer. Our analysis focuses 

on living consumers whose records will only be unscored for these two reasons. 

6 Though credit records also contain the month and day of birth for consumers, the CCP does not include this 

information to help maintain the privacy of the consumers in our sample. 
9 CFPB DATA POINT: CREDIT INVISIBLES 

tract as low, moderate, middle, or upper income, depending on whether the tract’s relative 

income is below 50 percent, between 50 and 80 percent, between 80 and 120 percent, or above 

120 percent. 

2.2 Dataset Creation 

Estimating the number of credit invisibles is complicated by the fact that almost no data exists 

specifically for this population. Some datasets that collect data on a representative sample of the 

entire population, like the Survey of Consumer Finances or the ACS, certainly include 

information on credit invisibles, but do not collect information that allows one to determine 

which sample observations are credit invisible. Datasets like the CCP generally have good 

information about consumers with credit records but by definition cannot include consumers 

without credit records. 

Our approach is to estimate the number of credit invisibles by comparing the adult population 

in the U.S. from the 2010 Decennial Census with an estimate of the number of adults who have a 

credit record at the NCRAs. While this may seem straightforward, it is actually a complex 

undertaking. The reason is that many consumers have multiple credit records within the data of 

the NCRAs. As a result, comparing the number of credit records maintained by the NCRAs with 

the U.S. population would be misleading. For example, the CCP in 2010 contained 4.91 million 

credit records. Given the 1-in-48 sampling rate used by the CCP, this implies that there were 

about 236 million credit records at the NCRA, more than the 235 million adults in the U.S. 

according to the Census. By itself, this would suggest that there are no consumers without credit 

records. 

The reason that some consumers have multiple credit records is the existence of “fragment 

files.” These are credit records containing a portion of a consumer’s credit history that exist 

outside of the consumer’s primary file. Take for example a consumer with a credit record who 

opens a new credit card. When the lender or servicer first reports the account, the NCRA 

attempts to match it with the correct credit record using a proprietary algorithm. If, based on 

that algorithm, the NCRA is unable to find any credit records that match, or is unable to find a 

unique match, perhaps reflecting erroneous or incomplete information reported with the new 

account, then the newly reported credit card will be placed in its own credit record. Most of 

these fragment files are temporary. Over time, as more information comes in, the NCRA may 

determine that the credit record is a fragment and that the accounts in the record belong to a 
10 CFPB DATA POINT: CREDIT INVISIBLES 

consumer with an existing credit record. When this happens, the information in the fragment 

file gets subsumed in the consumer’s primary credit record and the fragment file ceases to exist. 

Fragment files present an interesting challenge for estimating the number of consumers who 

have credit records at the NCRAs. An accurate measurement requires pruning from the data 

those records that are likely to be fragment files; otherwise, we will overestimate the number of 

consumers with a credit record and underestimate the number of credit invisibles. For example, 

as discussed above, without any pruning the CCP (or other data based on credit records) would 

imply that all Americans have credit records or, possibly, that there are more records than 

people. 

Our process of cleaning the data involves the following exclusions. First, since we are 

comparing credit records to the U.S. population, we exclude credit records that indicate the 

consumer was living outside of the fifty states. Second, we exclude the credit records of 

consumers who appear to be deceased in December 2010. 

We then use hindsight to identify fragment files. We discard any credit record from December 

2010 that does not appear in the December 2014 data as well, suggesting that the record had 

been purged from the database or merged into another record during this time. Finally, we 

exclude any credit record that had no reported year of birth in either December 2010 or 

December 2014. Birth dates tend to be an important characteristic in matching accounts to 

credit records. Accounts that lack this information are less likely to be (uniquely) matched to an 

existing credit record and are more likely to be placed into a fragment file. Any CCP record that 

was missing year-of-birth information for four years should also have been missing date-of-birth 

information in the records maintained by the NCRA over this period, which suggests that these 

records are fragments containing accounts that could not be linked.7 We discuss these 

exclusions in more detail in Appendix A. 

Once we have removed the likely fragment files, we estimate the number of credit invisibles in 

each tract as the difference between the tract’s adult population according to the 2010 Decennial 

 

7 This is further supported by the prevalence of authorized user accounts in these files. Authorized users are people 

who are permitted to use a revolving account, like a credit card, without being legally liable for any of the charges 

that are incurred. Lenders generally do not require a lot of detail on these consumers and, based on our 

conversations with industry participants, their accounts often end up in fragment files as a result. For more 

information on authorized user accounts and the issues involved, see Brevoort, Avery, and Canner (2012). 
11 CFPB DATA POINT: CREDIT INVISIBLES 

Census and our estimate of the number of consumers in the tract who have a credit record. Since 

the 2010 Decennial Census provides tract-level information on population by age, we are able to 

calculate the number of credit invisibles for each of thirteen different age groups: Eleven age 

groups are defined using five-year spans of ages from 20 through 74 (i.e, 20 to 24, 25 to 29) and 

the remaining two contain 18-to-19 year olds and those 75 or older. We also estimate the 

number of consumers with insufficient-unscored and stale-unscored credit records from the 

CCP for each tract at each of the 13 age groups. 

These estimates of the number of credit invisibles and consumers with unscored credit records 

depend crucially on the exclusions described earlier in this section. To the extent that some of 

the excluded credit records may have been the primary records of consumers, our estimates of 

the number of credit invisibles will be overstated and the number of consumers with a credit 

record (scored or unscored) will be understated. In contrast, if we have failed to exclude some 

credit records that are fragment files, then our estimates will tend to understate the number of 

credit invisibles and potentially overstate the number of consumers with credit records. One 

exclusion that we considered imposing, but decided against, was removing consumers whose 

only item on their credit record was a third-party debt collection or public record (such as a tax 

lien). While some of these are likely fragment files, our analysis suggested that removing these 

would likely exclude too many primary files. As a result, we believe that our estimate of the 

number of credit invisibles is likely low and our estimate of the number of consumers with 

unscored credit records likely overstated slightly since debt-collection-only or public-recordonly credit records tend to be unscored. We provide additional detail on the consequences of 

each of these exclusions for our estimates of the number of credit invisibles and consumers with 

unscored records in Appendix A. 

 

 
12 CFPB DATA POINT: CREDIT INVISIBLES 

3. How Many Americans Have 

Limited Credit Histories? 

Our estimates suggest that approximately 188.6 million Americans have credit records at one of 

the NCRAs that can be scored by the commercially-available model that informs our analysis. 

This represents over 80 percent of the adult population. An additional 19.4 million Americans, 

representing 8.3 percent of the adult population, have credit records that cannot be scored. 

These are almost evenly split between consumers with credit records that are insufficient 

unscored (9.9 million) and those that are stale unscored (9.6 million). The remaining 11 percent 

of adults, or about 26 million Americans, are credit invisible. 

Credit history is something that consumers establish over the course of a lifetime. As a result, 

one would expect the problem of limited credit history to be more concentrated among the 

young. This pattern is observed in the data. Panel (A) of figure 1 shows the share of consumers 

in each age group that are credit invisible, have unscored records because of insufficient 

information, or have unscored records because of a lack of recently reported information. As 

shown, over 80 percent of 18 or 19 year olds are credit invisible or have unscored records. This 

percentage drops substantially for older consumers, falling below 40 percent in total for the 20 

to 24 year old age group. After age 60, the number of consumers that are credit invisible or that 

have an unscored record increases with age. With our existing data, it is difficult to determine to 

what extent this reflects an age effect (a greater tendency of credit histories to shrink or become 

stale with age), a cohort effect (in which people born earlier than 1950 had thinner credit 

histories over the course of their lives, possibly reflecting less credit reporting during the periods 

of their lives when they were actively using credit), or some combination. 

 
13 CFPB DATA POINT: CREDIT INVISIBLES 

FIGURE 1: INCIDENCE AND NUMBER OF CONSUMERS THAT ARE CREDIT INVISIBLE OR HAVE RECORDS 

THAT ARE UNSCORED 

The data shown in panel (A) also indicate that the causes of an unscored credit record differ 

substantially by age. The share of consumers with an unscored credit record because of an 

insufficient credit history declines with age. Only a small percentage of consumers aged 65 or 

older have records that are unscored because of an insufficient history; instead, most of the 

unscored records for these older consumers are the result of a lack of recent information. 

Interestingly, having a stale-unscored credit record is not strongly related to age. In fact, the 
14 CFPB DATA POINT: CREDIT INVISIBLES 

incidence of a stale unscored record is higher for consumers aged 25 to 49 than it is for 

consumers older than 50. 

As this suggests, most consumers that are credit invisible or that have an unscored credit record 

are young. Panel (B) of figure 1 shows the distribution of the number of consumers who are 

credit invisible or have unscored records. Over 10 million of the estimated 26 million credit 

invisibles are younger than 25. Consumers in this age group also account for a disproportionate 

share of insufficient-unscored credit records. In contrast, most consumers with stale-unscored 

records are middle aged. Consumers aged between 25 and 50 account for over half of staleunscored credit records. 

Other characteristics besides age may also affect the likelihood of being credit invisible or having 

an unscored credit record. Among these is income. If higher-income consumers have an easier 

time qualifying for traditional credit, even without credit histories, then they may be more likely 

than lower-income consumers to open credit cards, auto loans, or other forms of credit that are 

frequently reported to the NCRAs. Relatedly, if lower-income consumers have a more difficult 

time qualifying for traditional credit and, as a result, rely on non-traditional sources like payday 

or auto-title lenders, then this will exacerbate the differences by income as these non-traditional 

sources of credit generally do not report information to the NCRAs. 

Exploring the relationship between income and the incidence of being credit invisible or having 

an unscored record is complicated by the fact that credit records do not contain income 

information. As a result, we do not know the income levels of the consumers whose credit 

records are in the CCP and, thus, rely on the relative income of each census tract as an 

alternative measure. Panel (A) of figure 2 shows the number of consumers that are credit 

invisible or have an unscored credit record who live in census tracts with each of the four 

relative income levels: low, moderate, middle, or upper. As shown, middle-income tracts 

account for a larger portion of the credit invisible and unscored population than any of the three 

other income groups. Consumers from low- and upper-income neighborhoods, in particular, 

make up a notably smaller share of the credit invisible and unscored population. 

 
15 CFPB DATA POINT: CREDIT INVISIBLES 

FIGURE 2: NUMBER AND INCIDENCE OF CONSUMERS THAT ARE CREDIT INVISIBILE OR HAVE AN 

UNSCORED CREDIT RECORD BY CENSUS TRACT INCOME LEVEL 

By and large, these numbers reflect varying population sizes in each income category. There are 

many more consumers in middle-income tracts than in low-income tracts, so it is not surprising 

that so many of these invisible and unscored consumers come from middle-income tracts. 

Instead, if we look at the share of consumers who are credit invisible or have an unscored credit 

record at each of these income levels, shown in panel (B), we see a very different pattern. 

Almost 50 percent of consumers in low-income tracts appear to either lack a credit record 

entirely or have an unscored credit record (mostly because of an insufficient credit history). At 

higher-income levels, this incidence falls sharply. In comparison, fewer than 10 percent of 

consumers in upper-income tracts are credit invisible or have unscored records. So while lowincome tracts appear to comprise a relatively small share of the credit invisible or unscored 

population (about 5 million of the total 45 million consumers), this represents a significant 

share of the population in those tracts. 

 
16 CFPB DATA POINT: CREDIT INVISIBLES 

4. Patterns of Limited Credit 

History by Race and 

Ethnicity 

4.1 Patterns by Race or Ethnicity 

Another characteristic that has been mentioned in connection with consumers that are credit 

invisible or have unscored credit records is race or ethnicity. As with income, credit records do 

not contain any information about the race or ethnicity of the consumer. As a result, we do not 

observe this information for the consumers whose credit records are in the CCP and, unlike 

income, we cannot easily segment census tracts into different racial or ethnic groups. This 

analysis, therefore, requires a different approach than we used in the previous section. 

To explore how the incidence of being credit invisible or having an unscored record varies with 

race or ethnicity, we examine cross-tract variation in the racial composition of census tracts and 

in the number of consumers who are credit invisible or have unscored records. Specifically, for 

each tract, we estimate the number of consumers in each of the thirteen age groups who are 

credit invisible. We then use the racial mix of the tract in each age group from the 2010 

Decennial Census to estimate the racial or ethnic mix of credit invisibles, assuming for these 

purposes that the distribution of credit invisibles in any given tract is proportionate to the racial 

and ethnic composition of the tract (i.e., that members of each racial or ethnic group in a given 

tract have an equal chance of being credit invisible). For example, if we find that a tract has 100 

credit invisibles in a given age group, and that tract’s population in that age group is 15 percent 

Black, 10 percent Hispanic, 5 percent Asian, and 70 percent White, then we would assume that 

15 of these credit invisibles were Black, 10 were Hispanic, 5 were Asian, and the remaining 70 

were White. We make this calculation for each tract, at each age level, and aggregate the 
17 CFPB DATA POINT: CREDIT INVISIBLES 

numbers nationally. We estimate the racial and ethnic mix of consumers with unscored records 

using the same method. 

FIGURE 3: NUMBER AND INCIDENCE OF CONSUMERS THAT ARE CREDIT INVISIBILE OR HAVE AN 

UNSCORED CREDIT RECORD BY RACE OR ETHNICITY 

The results of these aggregations are shown in figure 3. Panel (A) shows our estimate of the 

distribution of consumers who are credit invisible or have unscored records across the five 

different racial or ethnic groups used in this study. The patterns are largely consistent with the 

overall shares of these racial or ethnic groups in the population at large. Most consumers who 

are credit invisible or have unscored credit records are White. Minorities account for a smaller 

share of the population that is credit invisible or has an unscored record, largely reflecting the 

fact that minorities make up a smaller portion of the overall U.S. population. 

Panel (B) shows the percentage of consumers from each of the five racial or ethnic groups who, 

using our estimates, are credit invisible or have an unscored credit record. Whites are the least 

likely racial or ethnic group to be credit invisible or to have an unscored credit record, though 

the rates for Asians are almost identical. Blacks and Hispanics, as well as those included in the 

“Other” racial category, are notably more likely to be credit invisible or to have an unscored 
18 CFPB DATA POINT: CREDIT INVISIBLES 

record than Whites. Though Hispanics are slightly more likely than Blacks to be credit 

invisible, Blacks appear to be more likely than Hispanics to have unscored records.8

In calculating these estimates of the racial and ethnic mix of consumers with limited credit 

histories, we assumed that, within each census tract, consumers of each race or ethnicity had an 

equal likelihood of being credit invisible or having an unscored record. Our results suggest that 

consumers in census tracts with relatively more Blacks or Hispanics are more likely to be credit 

invisible or have an unscored credit record. Since we observe this pattern across tracts, it is 

likely that a similar pattern holds within tracts as well. If true, our estimate of the number of 

Blacks or Hispanics who are credit invisible or have an unscored credit record is likely 

underestimated and the number of Whites or Asians overestimated. 

4.2 Racial or Ethnic Patterns by Age 

The results by race or ethnicity suggest that minority populations, other than Asians, are 

generally more likely to be credit invisible or have unscored credit records. As shown in the 

previous section, the incidence of these forms of minimal credit history is strongly correlated 

with age. To better understand how these differences across racial or ethnic groups emerge over 

the course of a lifetime, we also compare the incidence of being credit invisible or having an 

unscored record by age across the different racial or ethnic groups. Because we do not observe 

how credit records change with age, we are unable to disentangle the effects of age from cohort 

effects, such as the different macroeconomic environments that consumers in different age 

groups have faced. Nevertheless, while not conclusive, the results of this analysis may provide 

some evidence about whether these differences emerge at young ages and, if so, whether they 

tend to dissipate with age. 

 

8 One factor that may distort these figures is the undercounting of minorities (and overcounting of Whites) in the 

2010 Decennial Census. The Census Bureau’s post-enumeration survey for the 2010 Census found that, while the 

2010 Census was the most accurate to date, the White population may have been overcounted by 0.85 percent. 

Blacks may have been undercounted by 2.1 percent and Hispanics by 1.5 percent (Mule, 2012). These results would 

suggest that we are overcounting the number of White credit invisibles and undercounting the number of Black or 

Hispanic credit invisibles. These changes would not have had a notable effect on the number of consumers with 

unscored records, though the percentage of Blacks and Hispanics unscored records would be slightly smaller. 
19 CFPB DATA POINT: CREDIT INVISIBLES 

FIGURE 4: INCIDENCE OF BEING CREDIT INVISIBLE BY AGE AND RACE OR ETHNICITY 

The share of consumers in each age group who are credit invisible is shown for four racial or 

ethnic groups in figure 4.9 For most racial or ethnic groups, the age patterns are very similar, so 

we present the results slightly differently to sharpen the contrasts. The graph on the left, panel 

(A), shows the results for Whites, which we use as the baseline group. The results suggest that 

the incidence of being credit invisible is very high for 18-19 year olds, but then falls sharply. The 

share holds relatively steady after age 25, until it begins to increase with age after 60. 

The graph on the right, panel (B), which is shown at a magnified scale, shows the results for 

Asians, Blacks, and Hispanics relative to the pattern for Whites. For example, among 

consumers who are aged 25-29, Blacks are 5 percentage points more likely than Whites in the 

same age group to be credit invisible. From the left graph, panel (A), we can see that about 6 

percent of Whites are credit invisible, which means that 11 percent of Blacks are credit invisible 

at this age. 

 

9 Results for the “Other” racial group are omitted from the remaining graphs of this section to reduce the amount of 

clutter; however, they are provided in the tables in Appendix B. 
20 CFPB DATA POINT: CREDIT INVISIBLES 

The patterns in this graph indicate that Blacks and Hispanics are more likely than Whites to be 

credit invisible at almost every age. While we are unable to determine with our existing data 

whether these reflect age or cohort effects, these patterns suggest that the relatively higher 

incidences of being credit invisible for Blacks and Hispanics emerge at young ages and tend to 

persist over time. The difference between Whites and Asians is much less consistent across 

ages. Like Blacks and Hispanics, Asians younger than 30 or older than 60 are more likely to be 

credit invisible than are Whites; however, Asians aged 30 to 59 have a lower incidence of being 

credit invisible. This suggests that the relative equality between Whites and Asians in terms of 

the aggregate incidences of being credit invisible (shown earlier in table panel (b) of figure 3) 

conceals significant differences across ages. 

Figure 5 shows a similar analysis for the incidence of unscored credit records. The left panels 

show the incidence for Whites of having an unscored record because of an insufficient credit 

history, panel (A), or a lack of recent history, panel (C). The right panels, (B) and (D), show the 

patterns by age for the other three racial or ethnic groups relative to Whites for these two types 

of unscored records, respectively. 

The results indicate that the share of Whites with a credit record that is unscored because of an 

insufficient credit history declines steadily by age, as shown in panel (A). Blacks and Hispanics 

have consistently higher likelihoods of having an insufficient unscored credit record (panel (B)). 

The differences are largest at younger ages. While they decline for older consumers, Blacks and 

Hispanics of all ages are more likely than Whites to have an insufficient-unscored credit record. 

While young Asians are less likely, and older Asians more likely, than Whites to have an 

insufficient-unscored credit record, the gap between Asians and Whites remains less than one 

percentage point across all age groups. 

 
21 CFPB DATA POINT: CREDIT INVISIBLES 

FIGURE 5: INCIDENCE OF HAVING AN INSUFFICIENT-UNSCORED OR STALE-UNSCORED CREDIT 

RECORD BY AGE AND RACE OR ETHNICITY 
22 CFPB DATA POINT: CREDIT INVISIBLES 

The patterns by age for records that are stale-unscored are somewhat different. For Whites, the 

likelihood of having a stale-unscored record increases with age until around 25-34 (panel (C)). 

It then declines with age thereafter. The share of Black or Hispanic consumers who have a staleunscored record is consistently higher than Whites at almost all age levels (the exception being 

18-19 years of age, when the likelihood of having a stale unscored record is near zero for all 

consumers). This gap increases with age until the mid-40s and declines thereafter. While the 

gap with Whites declines at older ages, Blacks and Hispanics appear to be consistently more 

likely to have a stale-unscored credit record. 

Like the pattern observed for insufficient-unscored credit records, young Asians are less likely, 

and older Asians more likely, than Whites to have stale-unscored credit records. Again, 

however, the gap between Asians and Whites remains within 1 percentage point at all age levels, 

suggesting that the patterns for Asians and Whites are similar. 

Taken together, these results suggest that Blacks and Hispanics are more likely to be credit 

invisible or to have unscored credit records. These differences are observed for all age groups, 

which suggests that these differences emerge at young ages and persist over the lifetimes of 

these consumers. 

FIGURE 6: INCIDENCE OF HAVING A SCORED CREDIT RECORD BY AGE AND RACE OR ETHNICITY 
23 CFPB DATA POINT: CREDIT INVISIBLES 

The combined effects of being credit invisible or having an unscored credit record are shown in 

figure 6, which depicts the share of consumers at each age with a scored credit record. Again, 

the left panel shows the pattern by age for Whites and the right panel shows the relative patterns 

for Asians, Blacks, and Hispanics. 

Panel (A) of this figure shows that the share of Whites with a scored credit record increases 

sharply with age up to around age 30 and then increases more gradually through age 60. At 

older ages, the incidence of having a scored credit record decreases somewhat with age. Blacks 

and Hispanics, shown in panel (B), are less likely than Whites to have a scored credit record at 

very early ages. This gap widens with age, becoming greater than 10 percentage points for ages 

25-29 for both groups, and remains large thereafter, though it does narrow (particularly for 

Hispanics around 50 years of age, though this narrowing is not observed at older ages). As our 

earlier results would suggest, the pattern for Asians is somewhat different. At early ages, they 

are less likely to have scored credit records than are Whites; however, this gap shrinks during 

their 20s and disappears in their 30s to 50s, during which time they are more likely to have a 

scored credit record than Whites in the same age group. Asians older than 54, however, are less 

likely to have credit records than Whites. 

Overall, these patterns suggest that the problem of limited credit history affects all racial or 

ethnic groups. Nevertheless, Blacks and Hispanics appear more likely to be credit invisible or 

have an unscored record. These differences are observed across all age levels. 
24 CFPB DATA POINT: CREDIT INVISIBLES 

5. Conclusions 

The three NCRAs have traditionally been the sole source of information used to calculate credit 

scores like the scores produced by FICO or VantageScore. Consumers with limited credit 

histories established in the records of the three NCRAs generally have a harder time obtaining 

credit as a result because many lenders do not extend credit to consumers without a scored 

credit record or do so only in quite narrow circumstances. While there has been a lot of 

attention paid to the problem of limited credit history and to various forms of alternative data 

that might mitigate it, very little is known about the number of consumers who are affected and 

even less is known about their demographic characteristics. 

This report uses data from the CFPB’s Consumer Credit Panel and aggregate information from 

the 2010 Decennial Census and 2008-2012 American Community Survey, both conducted by 

the U.S. Census Bureau, to construct estimates of the number of consumers with limited credit 

histories. Our results suggest that there are 26 million adults in the United States without a 

credit record. This amounts to 11 percent of U.S. adults. Additionally, our results suggest that 

another 19 million adults (about 8 percent) have credit records that are considered “unscorable” 

by the commercially-available credit scoring model used in this analysis. These records are 

almost evenly split between those that are unscored because of an insufficient credit history (too 

few accounts) and those that are unscored because of a lack of recently reported credit history. 

Our results also suggest that there is a strong relationship between income and having a credit 

record. Almost 30 percent of consumers in low-income neighborhoods are credit invisible and 

an additional 16 percent have unscored records. These percentages are notably lower in higherincome neighborhoods. For example, in upper-income neighborhoods, only 4 percent of the 

population is credit invisible and another 5 percent has an unscored record. 

Additionally, our results suggest that there are significant differences in the incidence of having 

a limited credit history across racial and ethnic groups. While Whites and Asians are almost 

equally likely to be credit invisible or have an unscored record, the shares of Blacks and 

Hispanics with limited credit history are much larger. About 15 percent of Blacks and Hispanics 
25 CFPB DATA POINT: CREDIT INVISIBLES 

are credit invisible (compared to 9 percent of Whites and Asians) and an additional 13 percent of 

Blacks and 12 percent of Hispanics have unscored records (compared to 7 percent of Whites). 

This elevated incidence of being credit invisible or having an unscored credit record is observed 

across ages, suggesting that these differences across racial and ethnic groups materialize early in 

the adult lives of these consumers and persist thereafter. These results suggest that the 

problems that accompany having a limited credit history are disproportionally borne by Blacks, 

Hispanics, and lower-income consumers. 

 
26 CFPB DATA POINT: CREDIT INVISIBLES 

References 

Avery, Robert B., Paul S. Calem, Glenn B. Canner, and Raphael W. Bostic. 2003. “An Overview 

of Consumer Data and Credit Reporting,” Federal Reserve Bulletin 89(2): 47-73. 

Brevoort, Kenneth P., Robert B. Avery, and Glenn B. Canner. 2013. “Credit Where None is Due? 

Authorized-User Account Status and Piggybacking Credit,” Journal of Consumer Affairs

47(3): 518-47. 

Board of Governors of the Federal Reserve System. 2007. Report to the Congress on Credit 

Scoring and Its Effects of the Availability and Affordability of Credit. (Washington, DC: 

Federal Reserve Board). 

Consumer Financial Protection Bureau. 2014. Report on the Use of Remittance Histories in 

Credit Scoring. Available online at 

http://files.consumerfinance.gov/f/201407_cfpb_report_remittance-history-and-creditscoring.pdf. 

Experian. 2014. “Let there be Light: The Impact of Positive Energy-Utility Reporting on 

Consumers,” Experian White Paper. 

Experian RentBureau. 2014. “Credit for Renting: The Impact of Positive Rent Reporting on 

Subsidized Housing Residents,” Experian RentBureau White Paper. 

Feinstein, Jeffrey. 2013. “Alternative Data and Fair Lending,” LexisNexis White Paper. August. 

Available online at 

http://www.lexisnexis.com/risk/downloads/whitepaper/fair_lending.pdf (Last visited April 

15, 2015). 

FICO. 2015. “FICO, LexisNexis Risk Solutions & Equifax Joining to Generate Trusted 

Alternative Data Scores for Millions More Americans,” FICO Press Release, April 2. 
27 CFPB DATA POINT: CREDIT INVISIBLES 

Available online at http://www.fico.com/en/newsroom/fico-lexisnexis-risk-solutions-andequifax-joining-to-generate-trusted-alternative-data-scores-for-millions-more-americans04-02-2015 (Last visited April 15, 2015). 

Jacob, Katy, and Rachel Schneider. 2006. “Market Interest in Alternative Data Sources and 

Credit Scoring,” The Center for Financial Services Innovation, December. 

Mule, Thomas. 2012. “Census Coverage Measurement Estimation Report: Summary of 

Estimates of Coverage for Persons in the United States,” May 22, 2012. Available online at 

http://www.census.gov/coverage_measurement/pdfs/g01.pdf (Last visited, April 15, 2015). 

Schneider, Rachel and Arjan Schutte. 2007. “The Predictive Value of Alternative Credit Scores,” 

Center for Financial Services Innovation Report, November. 

Turner, Michael A., Alyssa Stewart Lee, Ann Schnare, Robin Varghese, and Patrick D. Walker. 

2006. “Give Credit Where Credit Is Due: Increasing Access to Affordable Mainstream 

Credit Using Alternative Data,” Political and Economic Research Council and the Brookings 

Institution Urban Markets Initiative Report. 

VantageScore. 2013. “VantageScore 3.0: Better Predictive Ability Among Sought-After 

Borrowers.” VantageScore White Paper Series. December. Available online at 

http://www.vantagescore.com/images/resources/VantageScore3-0_WhitePaper.pdf (Last 

visited, April 15, 2015). 
28 CFPB DATA POINT: CREDIT INVISIBLES 

APPENDIX A: 

Effect of Fragment File 

Exclusions 

Estimates of the number of credit invisibles or consumers with unscored credit records depend 

crucially on the decisions about which credit records in the sample to identify as likely fragment 

files. In this Appendix, we provide detail about the exclusions that were applied in pruning the 

data of likely fragment files and the effects that these had on our results. 

Before identifying likely fragment files, we excluded credit records for consumers living outside 

of the United States. Most of these were credit records for consumers living in Puerto Rico and 

other U.S. territories. About 44,000 records were excluded for this reason. We also excluded 

credit records that indicated the consumer was deceased in 2010. These exclusions were 

necessary to focus on the population of interest and make the credit record population as 

comparable to the Census data as possible. 

Once these exclusions were made, this left a sample with 4.7 million credit records. From these 

we removed two groups of credit records that we believed were most likely fragment files. The 

first of these groups included credit records that disappeared between December 2010 and 

December 2014. In total, there were 242,727 records excluded for this reason. These records 

were split into two subgroups. The first subgroup included 138,152 credit records that were 

identified as having been consolidated into existing credit records, which is a direct identifier of 

a fragment file. The second subgroup included an additional 104,575 credit records that, while 

we could find no record of having been consolidated into an older credit record disappeared 

during the four years. 

The second group included the credit records of consumers whose credit records were missing 

year-of-birth information in both December 2010 and December 2014. There were 153,152 

records excluded for this reason. Date of birth is an important factor used by the NCRAs in 

matching reported account information to credit records. The fact that these files had no yearof-birth information in the CCP indicates that the credit record maintained by the NCRA did not 
29 CFPB DATA POINT: CREDIT INVISIBLES 

have a date of birth and suggests that these files contain account information that the NCRA was 

unable to uniquely match to a primary file. Consistent with this, most of these credit records 

contain authorized user accounts. 

In addition to these exclusions, our estimates were also affected by records that were not 

excluded, but that might have contained a large share of fragment files. In particular, we 

considered excluding those credit records that only contained information reported by thirdparty debt collectors or information from public records. Based on conversations with industry 

participants, we believe that NCRAs have a more difficult time finding unique matches for 

information from these sources. This suggests that these types of records may contain a large 

number of fragment files. Nevertheless, we believe that excluding all such records would have 

excluded too many primary credit records and chose to include these records in our estimates. 

Table 1 shows the effect that each of these exclusions and inclusions had on our estimates of the 

number of consumers who are credit invisible or have an unscored credit record. The first line 

of the table shows our estimate presented in the body of this Data Point. The following lines 

show the effect that each exclusion or inclusion had on the overall estimate. For example, the 

decision to exclude credit records that were missing in 2014 because they likely were fragment 

files had the effect of decreasing our estimate of the number of scored records by 4.3 million and 

increasing our estimate of the number of credit invisibles by 11.7 million. Similarly, our decision 

to include credit records that contained only third-party debt collection accounts increased our 

estimate of the number of consumers with a scored credit record by 0.1 million and decreased 

our estimate of the number of credit invisibles by 2.1 million. 

 
30 CFPB DATA POINT: CREDIT INVISIBLES 

TABLE 1: EFFECTS OF SAMPLE EXCLUSIONS AND INCLUSIONS 

Scored 

Records 

Credit 

Invisibles 

StaleUnscored InsufficientUnscored 

Baseline Estimate 188.6 26 9.6 9.9 

Exclusions: 

Missing in 2014 (Total) -4.3 +11.7 -1.7 -5.7 

 Observed Merge -3.0 +6.6 -1.2 -2.4 

 Disappeared -1.2 +5.0 -0.5 -3.3 

Missing Age -6.5 +7.4 -0.4 -0.5 

Inclusions: 

Debt Collection Only +0.1 -2.1 +0.09 +1.9 

Public Record Only +0.01 -0.4 +0.01 +0.3 
31 CFPB DATA POINT: CREDIT INVISIBLES 

APPENDIX B: 

Data Used in Figures 

This Appendix provides the underlying data used to produce each of the figures in the text. 

TABLE 2: SHARE OF CONSUMERS THAT ARE CREDIT INVISIBLE OR UNSCORED, DATA FOR FIGURE 1(A) 

Age Group Credit Invisible Stale-Unscored InsufficientUnscored 

18 to 19 years 64.5 0.4 18.9 

20 to 24 years 20.2 3.8 11.5 

25 to 29 years 8.9 5.9 5.9 

30 to 34 years 5.5 6.1 4.9 

35 to 39 years 7.6 5.6 3.9 

40 to 44 years 5.1 5.4 3.4 

45 to 49 years 7.4 4.7 3.0 

50 to 54 years 6.4 4.0 2.4 

55 to 59 years 6.3 3.4 1.7 

60 to 64 years 2.7 3.1 1.3 

65 to 69 years 8.6 2.3 0.9 

70 to 74 years 11.1 2.0 0.6 

75 years and over 17.8 2.0 0.4 
32 CFPB DATA POINT: CREDIT INVISIBLES 

TABLE 3: THE NUMBER OF CONSUMERS THAT ARE CREDIT INVISIBLE OR HAVE AN UNSCORED CREDIT 

RECORD BY AGE, DATA FOR FIGURE 1(B) 

Age Group Credit Invisible Stale-Unscored InsufficientUnscored 

18 to 19 years 5.8 0.0 1.7 

20 to 24 years 4.3 0.8 2.5 

25 to 29 years 1.9 1.2 1.2 

30 to 34 years 1.1 1.2 1.0 

35 to 39 years 1.5 1.1 0.8 

40 to 44 years 1.1 1.1 0.7 

45 to 49 years 1.7 1.1 0.7 

50 to 54 years 1.4 0.9 0.5 

55 to 59 years 1.2 0.7 0.3 

60 to 64 years 0.5 0.5 0.2 

65 to 69 years 1.1 0.3 0.1 

70 to 74 years 1.0 0.2 0.1 

75 years and over 3.3 0.4 0.1 

TABLE 4: NUMBER OF CONSUMERS THAT ARE CREDIT INVISIBLE OR HAVE AN UNSCORED CREDIT 

RECORD BY CENSUS TRACT INCOME LEVEL, DATA FOR FIGURE 2(A) 

Income Group Credit Invisible Stale-Unscored InsufficientUnscored 

Low 3.7 0.9 1.2 

Moderate 8.4 2.8 3.1 

Middle 11.2 4.1 3.9 

Upper 2.6 1.8 1.7 
33 CFPB DATA POINT: CREDIT INVISIBLES 

TABLE 5: INCIDENCE OF BEING CREDIT INVISIBLE OR HAVING AN UNSCORED CREDIT RECORD BY 

CENSUS TRACT INCOME LEVEL, DATA FOR FIGURE 2(B) 

Income Group Credit Invisible Stale-Unscored InsufficientUnscored 

Low 28.9 6.8 9.5 

Moderate 17.6 5.8 6.5 

Middle 11.0 4.1 3.8 

Upper 3.6 2.5 2.4 

TABLE 6: NUMBER OF CONSUMERS THAT ARE CREDIT INVISIBLE OR HAVE AN UNSCORED CREDIT 

RECORD BY RACE OR ETHNICITY, DATA FOR FIGURE 3(A) 

Race or Ethnicity Credit Invisible Stale-Unscored InsufficientUnscored 

Black 4.0 1.6 2.0 

Hispanic 5.3 1.8 2.1 

Asian 1.1 0.4 0.4 

Other 0.7 0.3 0.3 

White 14.7 5.5 5.1 

TABLE 7: INCIDENCE OF BEING CREDIT INVISIBLE OR HAVING AN UNSCORED CREDIT RECORD BY RACE 

OR ETHNICITY, DATA FOR FIGURE 3(B) 

Race or Ethnicity Credit Invisible Stale-Unscored InsufficientUnscored 

Black 14.8 5.8 7.2 

Hispanic 15.8 5.5 6.4 

Asian 9.8 3.6 3.7 

Other 13.7 4.6 5.9 

White 9.4 3.5 3.2 
34 CFPB DATA POINT: CREDIT INVISIBLES 

TABLE 8: INCIDENCE OF BEING CREDIT INVISIBLE BY AGE AND RACE OR ETHNICITY, DATA FOR 

FIGURES 4(A) AND 4(B) 

Age Group White Black Hispanic Asian Other 

18 to 19 years 63.9 66.6 64.0 65.6 65.3 

20 to 24 years 18.0 22.0 23.5 26.1 21.0 

25 to 29 years 6.0 11.1 15.5 9.2 9.3 

30 to 34 years 3.3 6.7 11.4 2.5 5.5 

35 to 39 years 6.7 8.4 11.3 3.9 7.3 

40 to 44 years 4.0 7.3 8.6 1.4 5.6 

45 to 49 years 6.8 10.7 8.5 3.4 9.5 

50 to 54 years 5.6 10.7 7.5 3.7 7.9 

55 to 59 years 5.3 11.2 8.9 4.2 8.1 

60 to 64 years 1.5 8.0 6.6 3.0 4.6 

65 to 69 years 7.3 14.8 13.6 9.1 10.2 

70 to 74 years 9.4 17.8 18.0 15.5 13.4 

75 years and over 16.5 23.9 23.9 22.5 20.0 
35 CFPB DATA POINT: CREDIT INVISIBLES 

TABLE 9: INCIDENCE OF HAVING AN INSUFFICIENT-UNSCORED CREDIT RECORD BY AGE AND RACE OR 

ETHNICITY, DATA FOR FIGURE 5(A) AND 5(B) 

Age Group White Black Hispanic Asian Other 

18 to 19 years 18.0 19.9 20.9 17.1 18.9 

20 to 24 years 10.3 15.1 13.1 9.4 12.4 

25 to 29 years 5.0 9.1 6.7 4.7 6.3 

30 to 34 years 3.9 8.3 5.7 3.7 5.2 

35 to 39 years 3.1 6.7 4.8 2.8 4.4 

40 to 44 years 2.7 6.2 4.4 2.5 4.1 

45 to 49 years 2.3 5.6 4.0 2.4 3.5 

50 to 54 years 1.9 4.9 3.4 2.0 3.0 

55 to 59 years 1.3 3.8 2.7 1.6 2.2 

60 to 64 years 1.0 2.8 2.2 1.3 1.8 

65 to 69 years 0.7 2.2 1.8 1.1 1.4 

70 to 74 years 0.5 1.4 1.3 0.8 1.0 

75 years and over 0.3 1.0 0.9 0.6 0.8 
36 CFPB DATA POINT: CREDIT INVISIBLES 

TABLE 10: INCIDENCE OF HAVING A STALE-UNSCORED CREDIT RECORD BY AGE AND RACE OR 

ETHNICITY, DATA FOR FIGURE 5(C) AND 5(D) 

Age Group White Black Hispanic Asian Other 

18 to 19 years 0.4 0.4 0.5 0.4 0.4 

20 to 24 years 3.5 4.6 4.4 3.0 3.9 

25 to 29 years 5.4 7.4 6.6 4.4 6.1 

30 to 34 years 5.5 8.0 7.1 4.5 6.4 

35 to 39 years 5.0 7.5 6.8 4.3 6.0 

40 to 44 years 4.7 7.5 6.6 4.3 5.8 

45 to 49 years 4.1 6.9 6.2 4.1 5.2 

50 to 54 years 3.5 6.3 5.5 3.6 4.7 

55 to 59 years 2.9 5.4 4.8 3.3 3.9 

60 to 64 years 2.7 4.9 4.5 3.2 3.7 

65 to 69 years 2.0 3.7 3.7 2.7 2.9 

70 to 74 years 1.7 3.3 3.1 2.2 2.3 

75 years and over 1.8 3.1 2.9 2.0 2.3 
37 CFPB DATA POINT: CREDIT INVISIBLES 

TABLE 11: INCIDENCE OF HAVING A SCORED CREDIT RECORD BY AGE AND RACE OR ETHNICITY, DATA 

FOR FIGURE 6(A) AND 6(B) 

Age Group White Black Hispanic Asian Other 

18 to 19 years 17.7 13.1 14.6 16.8 15.4 

20 to 24 years 68.2 58.3 59.0 61.5 62.7 

25 to 29 years 83.6 72.4 71.2 81.7 78.3 

30 to 34 years 87.2 76.9 75.8 89.3 82.8 

35 to 39 years 85.3 77.4 77.2 89.0 82.3 

40 to 44 years 88.6 79.0 80.4 91.7 84.5 

45 to 49 years 86.8 76.8 81.3 90.1 81.8 

50 to 54 years 89.0 78.1 83.6 90.7 84.4 

55 to 59 years 90.5 79.6 83.6 90.8 85.8 

60 to 64 years 94.8 84.3 86.7 92.5 89.9 

65 to 69 years 90.0 79.3 80.9 87.1 85.5 

70 to 74 years 88.4 77.5 77.6 81.5 83.3 

75 years and over 81.3 72.0 72.3 74.9 77.0