July 2014

Report on the use of 

remittance histories in 

credit scoring
2 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

Table of contents

Table of contents.........................................................................................................2

1. Introduction...........................................................................................................3

2. Data........................................................................................................................6

3. Can remittance histories be used to score otherwise unscorable 

records? ..............................................................................................................11

4. Can remittance histories improve the scores of consumers with 

scorable records? ..............................................................................................18

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

6. References cited.................................................................................................28

Appendix A: ...............................................................................................................30

The model building process ............................................................................30

Appendix B:...............................................................................................................33

Multivariate analyses ......................................................................................33
3 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

1. Introduction 

Credit scoring uses statistical modeling to predict whether a consumer will default on his or her 

debts. The quantitative estimates that these methods produce, called “credit scores,” allow 

lenders to rank order consumers in terms of the credit risk they pose and are used pervasively in 

all aspects of consumer lending. As a result, having a good credit score is important for credit 

access. Moreover, since credit scores, or other scores generated from credit report information 

such as insurance scores, can be used for underwriting other financial services products, 

including checking accounts and property and casualty insurance, a good credit score and credit 

report may provide benefits beyond credit access. 

While there are many different types of scoring models, those based on credit history 

information, such as the FICO and VantageScore, are the most widely used. These models 

generally rely on the credit histories compiled by nationwide credit reporting agencies (NCRAs). 

These records detail the credit accounts each consumer has held (called “tradelines”), collection 

agency accounts, monetary-related public records (e.g., tax liens, bankruptcy filings), and 

records of inquiries made by lenders in connection with a consumer-initiated credit application.

Even among consumers who have credit records – and many do not – NCRA records may 

contain little credit history information. This will occur when the consumer has little or no 

recent experience with credit in the United States or predominantly uses “non-traditional” 

sources of credit (such as payday lenders or pawnshops), which generally do not furnish account 

information to the NCRAs (although they sometimes furnish information to specialty consumer 

reporting agencies). With little information available with which to gauge the consumer’s 

riskiness, such “thin file” consumers generally receive lower scores as a result. In some cases, 

the amount of information is so sparse that model builders will deem the credit record 

“unscorable” and no score will be generated. Having a thin or unscorable credit record generally 

reduces access to credit.

Despite their thin or unscorable records, many of these consumers may be creditworthy, 

meaning that they are likely to repay their debts. However, because of the limited credit history 
4 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

reflected in their credit records, credit scoring models cannot distinguish them from less 

creditworthy consumers who also have thin credit records. A frequently suggested solution to 

this problem is to supplement the credit history information of these consumers with additional 

data. Alternative sources of data, such as rental histories or utility payments, have been shown 

to increase the credit scores of many creditworthy consumers with thin files (Turner, et al., 

2006).

Another potential source of information that could enhance consumers’ credit scores is data on 

remittance transfers, which are certain electronic transfers of funds made by U.S. consumers to 

recipients abroad. Towards this end, Section 1073(e) of the Dodd-Frank Wall Street Reform and 

Consumer Protection Act required the Director of the Consumer Financial Protection Bureau 

(CFPB) to report within one year of the law’s enactment regarding, among other issues, the 

feasibility of and impediments to using remittance information in credit scoring. The Bureau 

issued that report on July 20, 2011.

In the report, the Bureau noted that there were a number of business and legal issues that stood 

as potential impediments to incorporating remittance history into credit files, including some 

relating to whether remittance transfer providers would be willing to act as data furnishers to 

NCRAs and others relating to whether NCRAs and those who build scoring models would be 

willing to devote the time and resources required to incorporate this information into credit files 

and to build credit scoring models using such data. The Bureau noted, however, that it would 

conduct further empirical analyses to better address the potential for remittance information to 

enhance consumer credit scores. In this report, we discuss the empirical research completed to 

date by CFPB staff regarding whether data on remittance transfers can enhance the credit scores 

of consumers. As discussed in our earlier report (CFPB, 2011), “enhancing the credit scores of 

consumers” can be interpreted as either raising the credit scores of consumers who send 

remittance transfers or as improving credit score predictiveness to more accurately reflect credit 

risk. Our analysis covers both interpretations. 

The analysis is conducted in two parts. The first part focuses on consumers who have 

unscorable credit records and evaluates whether information about remittance histories has the 

potential to improve the predictiveness of credit scoring models sufficiently to allow scores to be 

generated for records that are unscorable based on credit history alone. The results of this 

analysis suggest that remittance histories add very little to the predictiveness of a credit scoring

model for these consumers. 
5 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

The second part of the analysis examines the potential of remittance histories to raise the credit 

scores of consumers with scorable credit records. This part of the analysis evaluates whether, 

after controlling for credit scores, remittance history information is negatively associated with 

subsequent delinquency. If so, remittance histories have predictive value over and above the 

information currently incorporated in credit scores that would be expected to result in higher 

credit scores for consumers who send remittance transfers. The results of our analysis suggest 

that, to the extent they have additional predictive value, remittance histories are positively 

associated with delinquency. This appears unrelated to the remittance transfers themselves and 

instead results from selection effects that cannot be adequately explained by the data. Given the 

positive correlation, building a credit scoring model that includes remittance history 

information is unlikely to increase the credit scores of consumers who send remittance 

transfers.
6 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

2. Data

To address the issues specified in Section 1073(e), CFPB staff assembled a unique source of data 

that combined information about remittance transfers with consumer credit record data. To 

construct this dataset, the CFPB partnered with a large remittance transfer provider (RTP) that 

selected a random sample of 500,000 anonymous consumers who sent one or more remittance 

transfers during 2007 and for whom the RTP had contact information (the “remitter sample”). 

The sampling frame that was used to draw this sample was based on consumers (as opposed to 

remittance transfers), so that a consumer who sent many transfers had the same probability of 

being selected as a consumer who sent only one.

Once the remitter sample was selected, the RTP identified all transactions made by those 

consumers during the period January 1, 2007 to December 31, 2008. For each transaction, the 

RTP compiled the date and amount of the remittance transfer, the mode of payment (e.g., cash, 

credit card, debit card), the country to which the funds were transferred, and the manner in 

which the funds were received (e.g., cash or deposited into a bank account).

This information was then sent to one of the three nationwide credit reporting agencies 

(NCRAs). The NCRA attempted to match the consumers in the remitter sample with their credit 

records in the NCRA’s files. For those consumers that could be matched, the NCRA merged the 

complete credit record of the consumer with the RTP data and added a commercially available 

credit score, the VantageScore (version 2.0). The NCRA then removed any direct identifying 

personally identifiable information, such as name, address, and Social Security number and 

created a consumer ID number, solely for this study, that allows all of the remittance and credit 

history information associated with the same consumer to be identified. The files received by 

the CFPB contain the remittance histories and credit record information (when available) for 

each consumer in the remitter sample. None of the files received by CFPB included any direct 

identifying personally identifiable information about these consumers.

Of the 500,000 people in the remitter sample, only 212,532 (or 43 percent) were matched to a 

unique credit record. Several factors likely contribute to this low match rate. For example, 
7 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

people who send remittance transfers may be less likely to use the credit sources that trigger the 

creation of a credit record or some of them may have recently immigrated to the United States 

and had insufficient time to establish a credit history. Alternatively, the low match rate might 

reflect incomplete or inaccurate addresses or a lack of other information (such as Social Security 

numbers) that would have improved the match process. With the data available, it is difficult to 

determine the extent to which any possible cause is driving this low match rate.

In addition to the credit records of the remitter sample, the NCRA also supplied a sample of 

200,000 randomly selected, anonymous credit records (the “control sample”). Like the remitter 

sample, the control sample includes the entire contents of each credit record, again excluding 

any directly identifying personally identifiable information, and a VantageScore (version 2.0). 

Since we cannot determine whether any of the consumers in the control sample sent remittance 

transfers themselves, this sample does not represent the population of consumers that sent no 

remittance transfers. However, the control sample can be used to draw comparisons between 

the credit records of the consumers who sent remittance transfers through the RTP supplying 

the data and the general population. Because we have data on remitters from only one RTP, the 

remitter sample does not necessarily represent the population of remitters.

For both the remitter and control samples, credit records were supplied for two points in time, 

December 2008 and December 2010. We use the later of the two credit record draws to assess 

each consumer’s performance on credit obligations from January 2009 to December 2010 (the 

“performance period”). Performance over this period can be related to credit record 

characteristics, including the VantageScore, from December 2008 and remittance transfers 

made during 2007-2008 to assess the predictiveness of this information.

We assess credit performance during the performance period using the worst performance on 

any of the consumer’s new or existing accounts, which is a commonly used metric in evaluating 

credit scoring models (Board of Governors of the Federal Reserve System, 2007). “Existing 

accounts” are those that were open and in good standing at the start of the performance period 

(December 2008) and “new accounts” are those opened during the first six months of the 

performance period (January to June 2009).

Each account’s performance is classified in one of four ways: “on time,” “defaulted,” 

“indeterminate,” or “not applicable.” Accounts that had at least one payment due during the 

performance period and where all payments were made as scheduled are classified as “on time.” 

Accounts that were 90 or more days past due or worse during the performance period are 

classified as “defaulted.” Other accounts are classified as “not applicable” if they had no 
8 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

reported payments due during the performance period or as “indeterminate” if they were 30 or 

60 days past due, but no worse. This classification scheme – including the classification of 

accounts that were 90 or more days past due as “defaulted” – is commonly used by industry in 

building and validating credit scoring models (Board of Governors of the Federal Reserve 

System, 2007). 

Performance for each consumer is similarly classified as on-time, defaulted, indeterminate, or 

not applicable based on the consumer’s worst performing account. Consumers with one or more 

defaulted accounts are considered to have exhibited “defaulted” performance. Any consumer 

with at least one on-time account, but no defaulted or indeterminate accounts, is classified as 

having had “on-time” performance. Otherwise, consumers are considered to have exhibited 

“indeterminate” or “not applicable” performance depending on whether any of their accounts 

was classified as indeterminate. Following standard industry practice in building and evaluating 

credit scoring models, when evaluating the predictiveness of credit record or remittance 

information, we focus on those consumers whose performance could be classified as on-time or 

defaulted and exclude consumers with indeterminate or not applicable performance.
9 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

TABLE 1: SUMMARY STATISTICS FOR CONTROL AND REMITTER SAMPLES

Control Sample Remitter Sample

Number of Consumers 200,000 500,000

 With Credit Records 200,000 212,532

 Deemed “Unscorable” by VantageScore 50,892 25,749

Average VantageScore 754 685

Average Number of Accounts 10.4 10.9

Average Age of Oldest Account (Years) 16.5 9.9

Average Age of Newest Account (Years) 4.0 1.6

Average Percentage of Accounts 

Ever 30+ Days Past Due 31.8 38.6

Percentage of Consumers 30+Days Past Due 

in the Last 6 Months 20.7 36.6

Percentage of Consumers With a Mortgage 37.0 33.7

Average Number of Credit Cards 3.9 4.2

Average Credit Card Utilization 

(Balance-to-Credit Ratio) 16.5 26.7

Average Credit Limit on Credit Cards 11,847 8,367

Average Number of Inquiries in Last 2 Years 1.5 3.2

Summary statistics for the control sample and for that portion of the remitter sample that could 

be matched by the NCRA are provided in Table 1. In comparing these groups it is important to 

bear in mind that, as previously noted, the remitter sample is derived from one RTP and is not 

necessarily representative of all remitters. Also, the statistics in Table 1 do not control for 

income, age, or other characteristics that might help explain some or all of the differences 

observed between the remitter and control samples.

With those caveats in mind, we note that consumers in the remitter sample with matched credit 

records have shorter credit histories on average than consumers in the control sample. On 

average, the oldest account on the credit record of consumers in the remitter sample is about 10 

years, 7 years younger than the average age of the oldest account for the control sample. 

Remitters in our sample also appear to have opened new accounts more recently. The average 
10 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

age of the most recently opened account is 1.6 years for remitters, compared to 4 years for the 

control sample.

The credit records of consumers in the remitter sample also tend to have more evidence of past 

payment delinquencies with respect to the types of obligations reported to NCRAs. On average, 

the credit records of remitters in our sample indicate that they had been 30 or more days past 

due or worse at some point on 38.6 percent of their accounts, compared to 31.8 percent for the 

control sample. Remitters in our sample also are more likely to have been recently delinquent 

on accounts. Thirty-seven percent of remitters have been at least 30 days past due on one or 

more accounts in the last six months, compared to 21 percent of the control sample.

Despite these differences, the credit records of consumers in the remitter sample are similar to 

those of the control sample in several respects (though all the differences in means for the 

remitter and control samples are statistically significant at the 1 percent level for every variable 

shown in Table 1). Consumers in the remitter sample have about 0.5 more accounts on average 

than consumers in the control sample, which indicates that, despite their shorter histories on 

average, the credit records of remitters whose credit records could be identified are not 

“thinner” than the control sample. Both groups are about equally likely to have a mortgage (34 

percent of remitters and 37 percent of the control sample have mortgages) and have about the 

same number of credit cards (4 each), though remitters use a larger share of their available 

credit. Remitters use, on average, 26.7 percent of their credit lines, compared to 16.5 percent for 

the control sample. 

One difference between the consumers in the remitter and control samples is their credit scores. 

This study uses the VantageScore (version 2.0), which is a credit score produced by 

VantageScore Solutions, LLC. The VantageScore covers a range from 500 to 990, with higher 

numbers indicating a lower likelihood of default. Among those consumers with scorable credit 

records, credit scores for remitters are lower. The average VantageScore for remitters with 

scorable records is 685, about 69 points below the average VantageScore for consumers with 

scorable records in the control sample. The lower credit scores for consumers in the remitter 

sample with scorable credit records suggest that accessing credit will be more difficult for these 

consumers. 
11 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

3. Can remittance histories be 

used to score otherwise 

unscorable records?

A group that might benefit from the use of remittance histories in credit scoring models is 

consumers whose credit records are currently considered unscorable. There is no single 

definition of what makes a credit record unscorable; indeed, the definition of an “unscorable 

record” differs across scoring models and any scoring model’s exact definition is generally 

treated as proprietary and not publicly disclosed. Generally speaking, however, credit records 

with too few accounts or too little recent activity are treated as unscorable by credit scoring 

models.

There are several reasons why industry model builders might consider credit records like these 

unscorable. One reason is that the records may provide insufficient information to distinguish 

consumers who are likely to repay their debts on time from consumers who are more likely to 

default. If remittance histories can provide sufficient additional information to allow lower-risk 

consumers to be identified and separated out from higher-risk consumers, then scoring models 

incorporating both credit and remittance histories might be able to expand the universe of 

scorable credit records. Such models might increase the willingness of lenders to extend credit 

to consumers with otherwise unscorable records, thereby increasing access to credit for these 

consumers.

In this section, we evaluate whether including remittance histories in credit scoring models is 

likely to increase the predictiveness of the models enough to warrant generating scores for 

otherwise unscorable credit records. We begin by estimating a credit scoring model that uses 

only credit history information. This model serves as a baseline for our analysis as it provides an 

estimate of the level of predictiveness that credit history information alone produces. We then 

evaluate how large of an increase in predictiveness results when remittance histories are added 
12 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

to the model. If remittance histories substantially increase model predictiveness, then this 

information may be sufficient to cause model builders to score such otherwise unscorable 

records. 

Our analysis is based on those records in the remitter sample that were matched to the NCRA 

data but are considered unscorable by the VantageScore model.1

 In the remitter sample, there 

are 25,749 such records, which constitute about 12 percent of the portion of the remitter sample 

that could be matched to credit records (and 5% of the total remitter sample). While in principle 

remittance histories may also have predictive value for consumers who could not be matched to 

a NCRA file, without such a match (57.5% of the remitter sample), repayment behavior cannot 

be observed from the NCRA data. Without observing repayment behavior, it is impossible to 

build or validate models that predict credit performance for these consumers. So, we exclude 

these observations from our analysis. This limitation in and of itself necessarily limits the 

usefulness of remittance histories in scoring otherwise unscorable records. 

Consumers in the control sample with unscorable credit records are likewise excluded from the 

analysis but for a different reason. Without the additional information that remittance histories 

provide, there is little potential to increase model predictiveness for these consumers. 

Remittance histories only directly affect the credit records of consumers who have sent 

remittance transfers. Any attempt to expand credit scoring models to incorporate remittance 

histories would likely focus exclusively on this population.

 

1 For credit records in our data that do not include a VantageScore, there is a code that indicates why the credit record 

is considered unscorable. One of the reasons given is that the credit record is for a deceased consumer. We assume 

that credit records for consumers who appear deceased in 2008 would remain unscorable even if remittance 

histories are available and consequently we do not include these credit records in our analysis. 
13 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

FIGURE 1: AN EXAMPLE OF CALCULATING THE KOLMOGOROV-SMIRNOV (KS) STATISTIC

We begin by estimating a “scorecard” specifically for otherwise unscorable credit records in the 

remitter sample. A “scorecard” is a statistical model that is estimated for a particular segment of 

scorable credit records. Most credit scoring models are comprised of several scorecards. For 

example, version 2.0 of the VantageScore, which is used in this study, is made up of 12 different 

scorecards (VantageScore, 2011). Scorecards allow model builders to vary the credit 

characteristics used as inputs into the scoring model, and how those characteristics affect the 

score, with the type of record being scored. For example, credit characteristics related to the 

amounts and frequency of past delinquencies may be very predictive for people who have had 

past delinquencies; however, they will have no value in distinguishing good from bad credit risks 

among records with no past delinquency. For this reason, separate scorecards are generally 

estimated for credit records with and without past delinquencies. Similarly, if remittance 

histories were to be used to score otherwise unscorable credit records, it is likely that model 

builders would develop a separate scorecard that incorporates information about remittance 

transfers.

To choose the credit characteristics that will be included in the scorecard, we use the modelbuilding methodology developed by the Board of Governors of the Federal Reserve System 

(2007) to evaluate the effects of credit scoring on the availability and affordability of credit. We 
14 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

use this methodology, the details of which are discussed in Appendix A, to construct a credit 

scoring model for the portion of the remitter sample with unscorable credit records in 2008. 

We develop this scorecard using only credit history information to provide a baseline for 

comparison to measure how much additional predictive power is provided by remittance 

histories. 

The predictive power of the model is assessed using the Kolmogorov-Smirnov (KS) statistic. The 

KS statistic is defined as the largest difference, across all credit scores, between the cumulative 

distributions of people with on-time or defaulted performance during the performance period. 

An example of the calculation of the KS statistic is shown in Figure 1 using the VantageScore and 

the control sample. The green line shows the cumulative distribution of VantageScores for 

consumers who defaulted during the performance period; that is, the green line shows, for each 

level of the VantageScore, the share of consumers who defaulted who had that score or lower. 

Similarly, the gray line shows the cumulative distribution of scores for consumers who had ontime performance. The largest vertical difference between these lines, which is shown by the 

vertical dashed line, is the KS statistic. In this example, the KS statistic is equal to 59.7.

KS statistics range between zero and 100 with higher values signifying greater predictive power. 

At the bottom end, a KS of zero implies that the score distributions are identical for people with 

on-time and defaulted performance, meaning that the score has no value in terms of separating 

consumers with on-time and defaulted performance. At the top end of the scale, a KS statistic of 

100 implies that the score has achieved perfect separation in that there exists a score below 

which all of the people with defaulted performance fall and above which are all of the people 

with on-time performance. Between these two extremes, a higher KS statistic implies that the 

model does a better job of identifying consumers who are less likely than others to become 

delinquent.

The scorecard constructed for this study generates a KS statistic of 43.4. Comparing this KS 

statistic with those generated by other credit scores is complicated by the fact that this statistic 

was calculated using the same sample of consumers used to estimate the scoring model. 

Normally, model developers would make use of a “holdout sample,” which is a portion of the 

sample of data that is used to evaluate the fit of the model but that is not used in estimation. 

Ideally, this holdout sample would include not only a different sample of consumers but would 

also represent a different time period. KS statistics produced from the sample used to estimate 

a model are often substantially higher than KS statistics generated from a holdout sample or a 

sample from a different time period. Unfortunately, the number of consumers in the remitter 

sample with unscorable credit scores and on-time or defaulted performance is too small to 
15 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

generate both estimation and holdout samples. As a result, this KS statistic likely overstates the 

predictiveness of this model.

While not a perfect comparison, we can compare the KS statistic produced for this scorecard 

with the KS statistics produced for the three scorecards that comprise the credit scoring model 

developed by the Board of Governors of the Federal Reserve System (2007) using the same 

methodology (the “FRB base model”). Like the scorecard developed here, the FRB base model 

was developed and evaluated without a holdout sample. The three scorecards of the FRB base 

model produced KS statistics of 53.5, 61.7, and 72.4.2 The largest of these KS statistics was 

produced by the scorecard for credit records with two or fewer tradelines (the “thin” scorecard). 

Since the population we built our model for is also disproportionately comprised of credit 

records with two or fewer tradelines, this scorecard is probably the most comparable. But 

regardless of which scorecard is used for comparison, the credit scoring model developed here 

produces a KS statistic that is significantly lower. While there is no generally accepted threshold 

for an acceptable KS, the low KS statistic generated by this model is consistent with the fact that 

these credit records are considered to be unscorable by the developers of the VantageScore 

(though there may be other reasons besides a poor model fit that contributed to this decision).

Having established this baseline for the predictive power of a scoring model using only credit 

histories, we create a new scorecard by adding variables to the model that reflect information 

about the remittance transfers (“remittance characteristics”). Our analysis focuses on two 

different remittance characteristics, the number and aggregate dollar amount of remittance 

transfers sent between January 1, 2007, and December 31, 2008. Since the model including 

both of these remittance characteristics together produces the higher KS statistic, we focus on 

the results from that model.

The model with remittance characteristics generates a KS statistic of 43.9, an improvement of 

0.5 over the KS statistic from the model built using only credit characteristics. Despite this 

 

2 The three scorecards that comprise the FRB base model include the “thin” scorecard, which scores credit records 

that have two or fewer tradelines; the “clean” scorecard, which scores credit records that have more than two 

tradelines without any indication of derogatory activity; and the “dirty” scorecard, which scores credit records with 

more than two tradelines and indicators of derogatory activity. The thin scorecard generated the highest KS 

statistic (72.4), followed by the dirty scorecard (61.7) and the clean scorecard (53.5).
16 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

improvement, however, the KS statistic for the model with remittance characteristics remains 

substantially below the KS statistics produced by the scorecards of the FRB base model. 

Interestingly, the weights assigned to the attributes for remittance history characteristics are 

negatively associated with default. This indicates that, among consumers who sent remittance 

transfers with otherwise unscorable credit records, those who sent more remittance transfers 

were more likely than those who sent fewer remittance transfers to have had on-time 

performance on their credit obligations over the performance period. The positive information 

that remittance transfers convey about this population holds regardless of whether the number 

of remittance transfers, the dollar amount of remittance transfers, or both are incorporated in 

the scorecard.

Because there are no hard and fast rules regarding the minimum goodness of fit statistic that 

would be necessary to make a scorecard commercially viable, we cannot say for certain whether 

the improvement in the KS statistic is sufficient to cause industry model builders to produce 

scores for otherwise unscorable consumers who send remittance transfers. However, the fact 

that the KS statistic generated by the model with remittance transfers remains substantially 

below any of those generated by the FRB base model suggests that industry model builders are 

unlikely to view the additional information as sufficiently valuable to warrant scoring the credit 

records of these consumers.

This analysis comes with important caveats. As mentioned at the beginning of this section, 

when deciding which credit records will be unscorable (such as, credit records with too few 

tradelines or no recent account activity), model builders consider more than just the goodness of 

fit provided by their models. Several other factors may weight against building a scorecard for 

remitters with otherwise unscorable credit records. 

One reason for concern about this model is the small share of consumers with unscorable credit 

records who had observable performance during the performance period. Of the 25,382 

unscorable credit records in the remitter sample, only 6.4 percent (or 1,632 records) had 

observable performance; that is, they have credit records indicating that they had open accounts 

during the performance period on which payment performance can be assessed. Since only 

credit records with observable performance can be used in the model estimation process, the 

scorecard estimated in this section uses only those 6.4 percent of unscorable records. (In 

comparison, we observe performance for 72.5 percent of consumers with VantageScores in the 

remitter sample.) 
17 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

The consumers who comprise this 6.4 percent are likely not representative of all remitters with 

unscorable credit records; instead, they are the portion of the unscorable remitter population 

who were able to obtain credit for at least a portion of the performance period. This may suggest 

that lenders, who may have access to information beyond what is contained in credit reports 

(such as, income or employment history), perceived these consumers as being less risky than 

those who did not have credit over this period. 

In such cases, where only a small fraction of a population has observable performance, 

scorecards may prove unreliable when they are deployed and used to make credit decisions for 

the entire population (in this case, remitters with otherwise unscorable credit records). For this 

reason, model builders are generally reluctant to develop scorecards unless performance can be 

observed for a sufficiently representative portion of the credit records for which the scorecard 

will apply. This suggests that even if the improvement in the KS statistic is viewed by industry 

model builders as material, they may still consider this population unscorable.

A second reason for concern about the use of remittance histories in credit scoring models is 

that incorporating this information may make the model more subject to “gaming.” The 

scorecard developed in this report suggests that, among people with otherwise unscorable credit 

records, more remittance transfers are correlated with lower levels of default. As a result, if this 

scorecard were put into use, consumers who send more remittance transfers would receive 

higher credit scores. A potential consequence of this would be that consumers who desire to 

artificially inflate their scores may be able to do so by sending money overseas for reasons 

unrelated to the remitter’s desire to assist the recipients of the money or the recipients’ need for 

the money.3 Done in advance of a credit application, this could allow consumers to obtain credit 

for which they would not otherwise have qualified. Model builders may be hesitant to use 

information that allows borrowers to quickly increase their score at relatively low cost.

 

3 In this respect, artificially increasing the number of remittance transfers to manipulate credit scores would be 

similar to purchasing authorized user account status on the credit accounts of others, a practice called 

“piggybacking.” In a piggybacking arrangement, the consumer pays a fee to a credit repair company to identify a 

third party who is willing to add the payer to their account as an authorized user in exchange for a portion of the fee. 

The payer becomes an authorized user in name only as they receive neither the account number nor an access 

device (such as a credit card). Nevertheless, by becoming an authorized user, the payer inherits the history of the 

account on his or her credit report. Done in advance of a credit application, this practice may allow consumers to 

obtain credit for which they otherwise would not have qualified. Many model builders, including VantageScore and 

FICO, have sought to revise their models to limit the ability of consumers to use this practice to increase their credit 

scores. For more information on piggybacking, see Brevoort, Avery, and Canner (2013).
18 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

4. Can remittance histories 

improve the scores of 

consumers with scorable 

records?

This section examines the potential for remittance data to improve the credit scores of 

consumers with already scorable records. Credit scores are empirically derived predictions 

about a consumer’s likelihood of default relative to other consumers; as a result, remittance 

histories will only affect scores to the extent that the information has value in predicting which

consumers will default above and beyond the information already contained in the credit reports 

used to calculate the score.4

In order to boost the credit scores of remitters, remittance histories would have to provide 

favorable predictive information above and beyond what is already contained in credit records. 

That is, remitters would have to “outperform” (or experience lower default rates than) other 

consumers with similar credit profiles. Whether or not this is true can be evaluated by 

comparing the default rates of consumers in the remitter sample during our performance period 

to the default rates of consumers in the control sample with identical credit scores. 

 

4 These empirically derived predictions of default apply to the time period used to estimate the model and are not 

predictions of the level of default in other time periods or other macroeconomic environments. Generally, credit 

history scores, like the FICO or VantageScores, are best interpreted as numbers that rank order a consumer’s 

likelihood of default relative to other consumers with otherwise similar risk characteristics during the same time 

period and in similar macroeconomic environments. The actual level of default associated with any score level can 

and does change over time.
19 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

FIGURE 2: CREDIT PERFORMANCE BY VANTAGESCORE FOR REMITTER AND CONTROL SAMPLES

This comparison is shown in Panel (A) of Figure 2. The gray line depicts the relationship 

between scores and credit performance for the control sample. As the downward slope of the 

line suggests, consumers with higher VantageScores were less likely to default. For example, 

almost 24 percent of consumers with a 700 VantageScore defaulted on one or more of their 

credit obligations during the performance period, compared to 1.8 percent of consumers with a 

900 VantageScore. The tendency of default rates to decline as scores increase suggests that the 

VantageScore rank ordered this population in terms of the default risk these consumers posed.

The green line in that panel shows the relationship between the VantageScore and subsequent 

performance for consumers in the remitter sample. Like the gray line for the control sample, 

remitters with higher VantageScores were less likely to default during the performance period. 

However, default rates are consistently higher for consumers in the remitter sample than they 

are for consumers in the control sample with identical VantageScores. This is shown in more 

detail (and in greater magnification) in Panel (B) of Figure 2, which depicts the difference in 

default rates between the control and remitter samples along with a 95 percent confidence 

interval. Default rates of remitters with scorable credit records were higher than those of the 

control sample (indicated by positive values) and those differences are statistically significant.

Information about the remittance transfers themselves, such as how many were sent or the total 

dollar amounts, could also provide useful information about a consumer’s likelihood of default. 

To assess the value of such information, we use multivariate analyses that can account for 

differences in remittance activity, as well as control for other factors like demographic 
20 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

characteristics and geography that may be related to default probabilities. We conduct several 

such analyses, the technical details of which are discussed in Appendix B. The results of these 

analyses suggest that default rates were 4.7 percentage points higher on average for remitters 

with scorable credit records than they were for consumers in the control sample with similar 

credit scores. These higher default rates suggest that adding remittance histories to credit 

scoring models, like the VantageScore, could decrease the credit scores of remitters with 

scorable credit records.

There are several reasons why sending remittance transfers might be associated with higher 

default rates. For example, it may be that, rather than providing evidence that someone has 

excess funds to repay debt, remittance transfers indicate that a larger share of the income of 

these consumers is committed to other uses. Much in the way a large mortgage payment may 

make it more difficult for a financially strapped borrower to repay credit card debt (assuming 

the borrower prioritizes paying his mortgage over his credit card), borrowers who send 

remittance transfers may have fewer resources to devote to repaying debts. This would be 

consistent with the positive association in the data between remittance transfers and default 

among consumers with a scorable credit record.

However, our analysis does not support this hypothesis. If remittance transfers suggest 

consumers have less disposable income, we might expect to see higher default rates for 

consumers sending more remittance transfers than for consumers sending fewer. To test this, 

we estimate models that control for the number or dollar amount of remittance transfers made. 

The results of these estimations (shown in columns 2 and 3 of Table B1 in Appendix B) suggest 

that, while generally positive, there is very little relationship between the amount of remittance 

transfers made (expressed either as the number or total dollar amount of remittance transfers) 

and credit performance.5

While consumers with scorable credit records that send many remittance transfers do not 

appear materially riskier than consumers who send fewer remittance transfers, these results 

imply that, among consumers with scorable credit records, a consumer who remits even a single 

 

5 The relationship between the dollar amount of remittance transfers and default is not statistically significant. While 

there is a statistically significant relationship between the number of remittance transfers and default, the 

coefficient on the number of remittance transfers suggests that this relationship is slight. Each remittance transfer 

sent increases the likelihood of default by 0.04 percent.
21 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

dollar is more likely to default than a consumer who sends no remittance transfers. This 

suggests that the predictive value provided by remittance histories derives less from the 

remittance transfers themselves and more from systematic differences in the characteristics of 

the consumers with scorable credit records who send remittance transfers. That is, remittance 

histories appear to be picking up a selection effect. 

Geography can explain some of this selection effect. The performance period evaluated in this 

study overlaps the Great Recession, a period characterized by notably higher delinquency rates 

on mortgages and other types of consumer credit. While the Great Recession affected all areas 

of the country, delinquency rates were notably higher in some states than in others. In 

particular, Arizona, California, Florida, and Nevada experienced large house price declines and 

elevated delinquency rates during this period (Olesiuk and Kalser, 2009). Consumers in the 

remitter sample disproportionally live in these four states. Almost one-third of the remitter 

sample with scorable credit records are from these states compared to slightly more than onein-five of the control sample. Controlling for the state in which the consumer lives reduces the 

performance difference between the remitter and control samples from 4.7 percent to 4.0 

percent.6 In other words, geography explains some of the performance difference between the 

remitter and control samples, but most of the difference appears to derive from other factors.

Additional analysis further supports the notion that the negative predictive information 

conveyed by remittance transfers is the result of a selection effect. Even if one believes that 

sending remittance transfers signals that a borrower has fewer financial resources to devote to 

credit obligations, there is little reason to believe that the destination of the remittance transfers 

should matter. Yet our analysis suggests that where the money is sent matters a lot. For some 

destination areas we find that remitters with scorable credit records were less likely to default 

than consumers with identical credit scores in the control sample (by 2.6 percentage points). In 

contrast, for other destination areas we find that remitters with scorable credit records were 

more likely to default than the control sample (by about 6.2 percentage points in both cases). 

The fact that the money’s destination has predictive value – despite there being no obvious 

reason why destination should determine credit performance – suggests that these location-

 

6 Instead of including fixed effects for each state, we estimate a separate VantageScore effect for each state. This 

approach is more flexible in that it allows different macroeconomic conditions in each state to have different effects 

by VantageScore. More information about how this was implemented is provided in Appendix A.
22 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

related differences reflect selection effects within the remitter sample. These selection effects 

may reflect differences among the remitter sample in characteristics that are not observable in 

credit records, such as income, employment, and education. The fact that we do not observe 

similar relationships between sending remittance transfers and default across remittance 

destinations suggests that the predictive value of remittance transfers overall has nothing to do 

with the remittance transfers themselves.

These differences in the observed predictive value of remittance transfers according to where the

money is sent also highlight a potential danger in using remittances histories in credit scoring 

models. The Equal Credit Opportunity Act (ECOA) prohibits discrimination in any aspect of a 

credit transaction on the basis of race, color, national origin, and other bases. The Fair Housing 

Act also prohibits discrimination in residential-real-estate-related transactions (e.g., making 

loans for purchasing, constructing, improving, repairing, or maintaining a dwelling) on the basis 

of race, color, national origin, or other bases. The use of remittance histories to make credit 

decisions may have a disproportionate negative impact on certain racial or national origin 

groups and thereby implicate fair lending concerns. Moreover, a lender’s consideration of the 

geographic destination of an applicant’s remittances as part of a credit decision could itself 

constitute discrimination on the basis of national origin.

In this study, we find differences in the predictive value of remittance transfers among otherwise 

similar consumers sending remittance transfers to different destination countries. Similarly, in 

their Report to Congress on Credit Scoring and Its Effects on the Availability and Affordability 

of Credit, the Board of Governors of the Federal Reserve System (2007) found differences in 

credit performance across racial and ethnic lines, for borrowers with identical credit scores. In 

both cases, the observed differences may be related to the substantial differences in income, 

wealth, employment, education, or other characteristics across racial and ethnic groups. They 

may also (or instead) be driven by differences in the type and terms of credit offered to different 

racial or ethnic groups. These factors are not reflected in credit records or directly captured by 

credit scores.

Without demographic information about the people in the remitter and control samples (or 

sufficient information to construct reliable proxy measures for demographic information), we 

cannot determine the extent to which (if any) the use of remittance histories in credit scoring 

models would have a differential effect by race or ethnicity. However, we may be able to roughly 

approximate the necessary data using the racial and ethnic composition of each consumer’s ZIP 

code using data from the Census Bureau. When these controls for neighborhood racial and 

ethnic composition are included, we find that they reduce the estimated effect of remittance 
23 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

transfers from 4.0 percent to 3.6 percent. Although the lack of individual-level demographic 

information renders these results inconclusive, they are consistent with the pattern we would 

expect to observe if the use of remittance histories in credit scoring models has a 

disproportionate effect by race or ethnicity. 

Other factors also could be contributing to the selection effect that remittance transfers appear 

to be picking up. One possibility is income, which is not a factor included in credit scoring 

models. If lower-income borrowers were to exhibit higher delinquency rates than borrowers 

with identical credit scores and higher incomes, and if remitters tend to have lower incomes 

than people who do not send remittance transfers, then the predictive value of remittance 

histories may reflect these income differences. Like race and ethnicity, the credit bureau records 

do not provide income information. However, we can use neighborhood income from the 

Census as an approximation. Controlling for neighborhood income, however, appears to have 

little effect on the estimated relationship between remittance transfers and delinquency.

Regardless of the source of the positive correlation between remittance histories and default for 

the consumers in our sample (i.e., consumers from one RTP who could be matched to a credit 

file from one NCRA), the correlation we find suggests that including remittance histories in 

credit scoring models appears unlikely to improve the credit scores of these consumers. 
24 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

5. Conclusions

The analyses documented in this report were conducted to further elucidate the issues discussed 

in the CFPB’s Report on Remittance Transfers (2011). The results of these analyses suggest that 

remittance transfers offer little potential to either allow scores to be generated for consumers 

with unscorable credit records or to improve the scores of consumers with scorable credit 

records.

The majority of consumers in our remitter sample could not be matched to a credit bureau 

record. This is because either the remitter did not have a credit record or the NCRA could not 

uniquely identify the remitter’s credit record with the information provided by the RTP. In both 

cases, there seems little likelihood that remittance histories can enhance the credit scores of 

these consumers. For remitters without credit records, remittance histories have little potential 

to produce scores for these consumers, since without credit records to provide credit 

performance measures model builders cannot construct credit scoring models for this 

population. For consumers who have credit records, but for whom the NCRA could not match 

the remittance data, the inability to match credit records with remittance data would preclude 

using remittance histories in scoring these individuals (though this situation would be mitigated 

if the use of remittance information in credit scoring incentivized RTPs to collect, and 

consumers to supply, more or better personally identifying information that made matching 

feasible). 

For the small segment of consumers in our sample with credit records that are considered 

“unscorable” by the VantageScore credit scoring model, remittance transfers appear to be 

associated with better credit outcomes. These consumers generally have too few tradelines 

reflected in their credit records or too little recent account activity for VantageScore to produce 

credit scores based on credit history alone. Despite the positive information that they provide, 

however, remittance histories appear to add little to overall model predictiveness, suggesting 

that industry model builders are unlikely to view the additional information as sufficiently 

valuable to warrant scoring the credit records of these consumers.
25 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

In contrast, for the consumers in our sample who already have credit scores (that is, those 

consumers whose credit records are considered “scorable” by the VantageScore credit scoring 

model), the information provided by remittance histories appears to be associated with worse 

credit outcomes, particularly relative to consumers in the control sample with identical credit 

scores. This implies that incorporating remittance history information in a credit scoring model 

would likely lower the credit scores of consumers who send remittance transfers and who have 

scorable credit records. The lower scores appear to result from selection effects that cannot be 

explained adequately by the data available but are unrelated to the remittance transfers 

themselves. Moreover, the data suggest that remittance transfers are likely correlated with race 

or ethnicity, indicating that the use of remittance histories in credit scoring models might raise 

concerns under fair lending laws.

The limited value that remittance histories appear to provide for predicting credit performance 

stands in stark contrast to the potential offered by other forms of alternative data. While 

concerns have been raised about their use, utility and rental payments have been shown to 

improve the predictiveness of credit scoring models (Turner, et al., 2006; CFPB, 2011).

A likely reason why these sources of information provide such different value in predicting 

performance can be found in the nature of the information itself. While rental and other bill 

payments are not credit per se, both fundamentally resemble credit in that they involve 

obligations to make a series of payments by established due dates. As such, bill payment 

histories allow the identification of those consumers who missed payments (and if on-time 

payment information is also reported, it allows consumers who paid on time to be distinguished 

from consumers who had no payments to make). It seems fairly straightforward to expect that 

on-time or missed payments for rent or utilities, much like on-time or missed payments for auto 

loans or credit cards, would convey information about the likelihood that a consumer will repay 

credit obligations in the future. However, it is important to note that there are unique dynamics 

around utility payments and payment assistance programs such that full utility credit reporting 

could harm low-income consumers and undermine the objectives of state utility consumer 

protections.7 As discussed in our earlier report (CFPB, 2011), remittance transfers lack this 

concept of a “missed payment,” which makes identifying consumers who are less likely to repay 

 

7 For additional information about the issues surrounding low-income consumers and full utility credit reporting, see 

National Consumer Law Center (2009).
26 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

debt difficult using these data, as suggested by the low predictive value of remittance histories 

observed in this study. Future efforts to enhance consumer credit scores by expanding data 

collections may prove more fruitful if they focus on activities that involve regularly scheduled 

payments, as opposed to activities involving voluntary payments like remittance transfers.

There are several caveats that go along with this analysis. First, the performance period covered 

by this study (January 2009 to December 2010) was unusual in that it was a period 

characterized by abnormally high unemployment and delinquency (particularly on mortgage 

credit). While we are confident that our results are sufficiently robust to hold in a variety of 

economic environments, the time period almost certainly had an effect on the magnitudes of the 

effects that we observe. 

Second, the remittance data used in this analysis was taken from a single RTP. Each RTP has its 

own customer base and these groups of customers may differ substantially in terms of their 

credit records or likelihood of repaying debt. Likewise, our remitter sample’s transaction 

histories may not represent the complete remittance histories of those remitters, as they may 

have sent some of their remittance transfers using other RTPs. While we have no reason to 

believe that our results are dependent upon the choice of RTP, it is possible that data from 

another RTP, from a representative sample of RTPs, or of consumers’ entire remittance histories 

might yield different results.

Third, our analysis evaluates only the predictive value of remittance histories for two large 

groups: consumers with and without scorable credit records. Analyses that focus on narrow 

subsets of these populations might reveal smaller groups for whom remittance histories convey 

useful, positive predictive information. Evaluating all possible such subgroups is well beyond 

the scope of this report and the lack of a clear relationship between remittance transfers and 

reduced delinquency in our aggregate results suggest that any such subgroups would likely be 

small. Nevertheless, we cannot rule out that there may be some consumers (either with scorable 

records or without) for whom remittance histories can enhance their credit scores.

Finally, our analysis has only examined the empirical issues with using remittance histories in 

credit scoring models and has not fully investigated or discussed the potential business and legal 

issues that would be related to doing so, many of which were discussed in detail in our earlier 

report (CFPB, 2011). In particular, we have not attempted to ascertain how remittance histories 

could be used in a credit scoring model in compliance with fair lending laws. If remittance 

histories are highly correlated with race, color, or national origin, three of the prohibited bases 

under such laws, using remittance histories could have a disproportionate adverse effect on 
27 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

those bases and might ultimately be found to have an unlawful disparate impact. While some of 

the questions related to any potential disparate impact are inherently empirical, without 

additional information about the demographic characteristics of the consumers in the control 

and remitter samples (or sufficient information to construct reliable proxy measures for such 

demographic characteristics), we cannot determine whether a disparate impact is likely to result 

or, if it results, whether the use of remittance histories would nonetheless be permissible under 

the disparate impact doctrine as “meet[ing] a legitimate business need that cannot reasonably 

be achieved as well by means that are less disparate in their impact.”8

 

 

8 12 C.F.R. pt. 1002, Supp. I, § 1002.6, ¶6(a)-2.
28 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

6. References cited

Avery, Robert B., Kenneth P. Brevoort, and Glenn Canner (2012) “Does Credit Scoring Produce 

a Disparate Impact?” Real Estate Economics 40(s1): S65-S114.

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

Scoring and Its Effects on the Availability and Affordability of Credit. Washington, 

DC: Federal Reserve Board.

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

Authorized User Account Status and ‘Piggybacking Credit,’” Journal of Consumer 

Affairs 47(3):518-547.

Consumer Financial Protection Bureau (2011) Report on Remittance Transfers, Washington, 

DC: Consumer Financial Protection Bureau. 

National Consumer Law Center (2009) Full Utility Credit Reporting: Risks to Low-Income 

Consumers. Available at 

www.nclc.org/images/pdf/credit_reports_full_utility_dec2009.pdf (last accessed 

03/04/2014). 

Olesiuk, Shayna M. and Kathy R. Kalser (2009) “The Sand States: Anatomy of a Perfect 

Housing-Market Storm,” FDIC Quarterly 3(1):30-32.

Turner, Michael A., Alyssa S. 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. Washington, DC: Political and Economic Research Council 

and The Brookings Institution Urban Markets Initiative.
29 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

VantageScore (2011) VantageScore 2.0: A New Version for a New World of Risk. Available at 

www.vantagescore.com/images/resources/VS2.0wp_FINAL_4.8.11.pdf (last accessed 

12/29/2013).
30 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

APPENDIX A:

The model building process

The credit scoring model used in Section 3 of this study was constructed using a methodology 

developed by the Board of Governors of the Federal Reserve System (2007) and refined by 

Avery, Brevoort, and Canner (2012). This algorithm was designed to mimic, to the extent 

possible, the process used by industry model builders in constructing credit history scoring 

models. While the process actually followed by industry model builders is not as mechanical as 

that described here, this approach allows us to be transparent about the decisions made in 

constructing and evaluating the model.

Like the data used by the Federal Reserve Board, the credit records used in this study include a 

large number of “credit characteristics.” Credit characteristics are variables that summarize the 

contents of each credit record. They are precalculated by the NCRA and supplied to model 

builders for use in constructing credit scoring models. The credit records provided for this study 

include 100 such credit characteristics. These credit characteristics include items like the total 

number of tradelines on the credit record and the total balance on open credit cards.

Each credit characteristic that is included in the model enters the scorecard as a series of 

“attributes.” An attribute reflects a specific range of values, with the attribute assigned a value 

of 1 if the value of the characteristic falls within the attribute’s specified range and zero 

otherwise. Attributes partition the space of possible values, so that for every credit record a 

single attribute is assigned a value of 1. All other attributes of that credit characteristic equal 

zero.

The first step in creating attributes is to determine whether the credit characteristic takes on 

“non-applicable” values. Non-applicable values arise when the value of a credit characteristic 

cannot be calculated. For example, if a consumer has not opened an account in the last 6 

months, the characteristic “overall balance to credit amount ratio on open trades opened in the 

last 6 months” cannot be calculated. In these cases, an attribute is created to reflect nonapplicable values. For credit characteristics that can always be calculated (such as “total number 
31 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

of trades,” which take zero values for consumers with no accounts) attributes for non-applicable 

values are irrelevant and are not included. 

Once an attribute for non-applicable values has been created (if necessary), attributes reflecting 

(applicable) values of the credit characteristic are created. The attribute creation process begins 

by creating a single attribute reflecting the full range of possible values of the characteristic. All 

possible subdivisions of this attribute into two candidate attributes, each covering a compact set 

of sequential values, are evaluated.9 This evaluation identifies the subdivision that is the most 

closely related to future credit performance, as determined by the mean square error. If the 

difference in mean performance between the two candidate attributes is statistically significant 

at the 5 percent level then the two candidate attributes replace the single attribute.

The algorithm then evaluates additional possible splits of each attribute (except the attribute for 

non-applicable values). All possible subdivisions of each attribute are again evaluated, though 

at this stage only those subdivisions that result in two candidate attributes that maintain 

monotonicity in mean performance levels across all of the characteristic’s attributes are 

considered. The attribute whose best subdivision reduces the mean squared error the most is 

replaced by its candidate attributes if the split is statistically significant at the 5 percent level. 

This process is then repeated until no additional statistically significant and monotonicitypreserving subdivisions are possible.

Once attributes have been created for every credit characteristic, the process of selecting which 

characteristics will comprise the model begins. When a credit characteristic is selected for the 

model, all of its attributes are included, except for the attribute reflecting the lowest value of the 

characteristic which serves as the omitted variable. Following standard model-building practice, 

we estimate a logit model subject to the constraint that the coefficients across the attributes of 

each credit characteristic must be monotonic (with the exception of the coefficient on the 

attribute for non-applicable values).

 

9 To be considered a candidate attribute, each attribute also had to have at least 5 observations that reflected on-time 

performance and 5 that reflected defaulted performance. Because of the monotonicity restrictions imposed on the 

average performance across attributes, this restriction was seldom binding and only then for attribute values at the 

highest and lowest values for each credit characteristic.
32 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

Credit characteristics are added to the model in a forward stepwise manner. We identify which 

of the 100 credit characteristics (along with an intercept) generates the best goodness of fit. 

Goodness of fit is assessed in this process using the divergence statistic. Like the KS statistic, 

the divergence statistic is a measure of a credit scoring model’s predictiveness. The credit 

characteristic that increases the divergence statistic the most is added to the model.

Next, the algorithm evaluates the remaining 99 credit characteristics to identify the one that, 

along with the credit characteristic that has already been added to the model, produces the 

largest increase in the divergence statistic. This credit characteristic is then added to the model. 

Credit characteristics are selected and added to the model in this manner as long as the 

marginal increase in the divergence statistic that results remains above a threshold of 0.75 

percent.

Once this process of adding credit characteristics is complete, each characteristic is again 

evaluated to ensure that its marginal contribution to the divergence statistic remains above the

threshold. This is done by removing each of the n credit characteristics that comprise the 

scorecard, recalculating the divergence statistic based on a model that includes only the 

remaining n-1 characteristics, and evaluating whether the implied percentage increase in the 

divergence statistic from including the characteristic remains above the threshold. Any credit 

characteristic whose marginal contribution to the divergence statistic is below the threshold is 

removed from the model. If any credit characteristics are removed, the algorithm then evaluates 

all of the omitted credit characteristics to assess whether their addition to the model would 

warrant inclusion.

The process of removing and adding credit characteristics continues until (a) each of the credit 

characteristics included in the model contributes to the divergence statistic a percentage 

increase on the margin that exceeds the threshold; and (b) none of the excluded characteristics 

would improve the divergence statistic by a percentage that exceeds the threshold if included in 

the model. Once these two conditions are met, the credit characteristics that comprise the 

model are set.

Once the credit characteristics that comprise the model have been determined, a final version of 

the logit model is estimated. The coefficients from this estimation are used to generate fitted 

values (or predictions of the probability of default) for each credit record. These fitted values are 

used as the credit scores produced by our scorecard. 
33 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

APPENDIX B:

Multivariate analyses

Several multivariate analyses were conducted to examine the predictive value of remittance 

transfers for subsequent credit performance for consumers with scorable credit records. In this 

section, we provide a technical description of the analyses that were conducted and present the 

results of the statistical estimations that were conducted in more detail.

The multivariate analyses compared the credit performance of consumers in the remitter and 

control samples who had scorable credit records. Only those consumers with “on-time” or 

“defaulted” performance during January 2009 to December 2010 are included in these analyses. 

Consumers with “indeterminate” or “not applicable” performance are excluded. The remaining 

sample contains 246,968 credit records. 

All of the multivariate analyses are conducted using ordinary least squares. These estimations 

were specified as 

Delinquencyi = f(VantageScorei) + Xiβ + εi (1)

where i indexes the consumer. The dependent variable, Delinquencyi, is an indicator variable 

that equals 1 if the consumer exhibited defaulted performance or 0 if on-time performance. 

Estimated equations include a fixed-effect for each VantageScore level, f(VantageScorei), so 

that these analyses are conducted “within score”; that is, we compare differences between the 

remitter and control samples using consumers with identical scores. The estimations also 

include a vector of characteristics of interest, Xi, a vector of coefficients that we estimate, β, and 

a random error term, εi.

The first set of analyses is comprised of three specifications that differ in terms of the 

characteristics that are included in Xi. The first estimation includes only a remitter indicator 

variable that takes on a value of 1 if consumer i is part of the remitter sample or 0 if part of the 

control sample. The second estimation includes this remitter indicator and an additional 

variable equal to the number of remittance transfers the consumer sent during 2007 and 2008. 

The third estimation includes the remitter indicator and a variable giving the total dollar 
34 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

amount of all remittance transfers made in 2007 and 2008. The results of these estimations are 

presented in Table B1.

Supplementary analyses control for the different macroeconomic environments across states 

during the performance period. In these estimations, we estimate separate relationships 

between the VantageScore and performance for each state. Specifically, these estimations are 

specified as

Delinquencyi = f(VantageScorei, Statei) + Xiβ + εi (2)

where Statei is the state where consumer i resides and other terms are as described in equation 

(1). The functional form used for f(VantageScorei, Statei) allows for a separate fixed effect for 

each combination of state and VantageScore. These estimations, therefore, implicitly compare 

the credit performance of consumers in the remitter sample with consumers in the control 

sample who have the same VantageScore and reside in the same state.

We conduct four estimations using the specification provided in equation (2), each differing in 

terms of the variables included in Xi. The first estimation includes only the remitter indicator 

used earlier. The second specification replaces the remitter indicator variable with a series of 

variables that indicate where consumers in the remitter sample sent their remittance transfers. 

Since there are too many destinations in the data to present results for them all, indicator 

variables are included for the five most common regions (Latin America and the Caribbean,

Variables (1) (2) (3)

Remitter Indicator 0.047***

(0.002)

0.044***

(0.002)

0.047***

(0.002)

Number of Remittance

 Transfers 0.0004***

(8.6e-05)

Amount of Remittance

 Transfers ($)

-1.91e-07

(2.3e-07)

Observations

R-squared

246,968

0.302

246,968

0.302

246,968

0.302

Notes: Standard errors are in parentheses. Statistical significance is indicated by asterisks 

where *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively. All 

estimations include a fixed effect for each level of the VantageScore.

TABLE B1: ESTIMATIONS OF CREDIT PERFORMANCE FROM JANUARY 2009 TO DECEMBER 2010
35 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

Eastern Asia, South Eastern Asia, Western Africa, and Southern Asia) and for all other 

destinations. 

The third and fourth estimations use data from the 2007-2011 American Community Survey 

about the characteristics of each consumer’s neighborhood. We use each consumer’s ZIP code 

to identify his or her neighborhood and match this to the Census data released for ZIP Code 

Tabulation Areas. The third estimation includes the remitter indicator variable and variables 

representing the racial and ethnic composition of each consumer’s neighborhood, which enter as 

a series of variables measuring the share of the neighborhood that is black, Hispanic, American 

Indian, Asian, Native Hawaiian, other, or multiracial. The fourth estimation includes these 

same variables and adds a variable representing the median household income in the 

neighborhood and the total dollar amount of remittance transfers made by the consumer during 

2007 and 2008 as a fraction of the median income in the neighborhood. The results of these 

estimations are provided in Table B2.
36 CFPB REPORT ON THE USE OF REMITTANCE HISTORIES IN CREDIT SCORING

Variables (1) (2) (3) (4)

Remitter Indicator 0.040***

(0.002)

0.036***

(0.002)

0.036***

(0.002)

Remittance Destination

 Latin America/Caribbean 0.062***

(0.002)

 Eastern Asia -0.026***

(0.004)

 South Eastern Asia 0.028***

(0.004)

 Western Africa 0.062***

(0.004)

 Southern Asia -0.002

(0.004)

 Other 0.027***

(0.003)

Race or Ethnicity

 Hispanic White 0.044***

(0.005)

0.069***

(0.005)

 Black 0.051***

(0.005)

0.073***

(0.005)

 American Indian -0.071

(0.061)

0.001

(0.062)

 Asian -0.072***

(0.009)

-0.084***

(0.009)

 Native Hawaiian 0.292**

(0.148)

0.350**

(0.148)

Neighborhood Income 5.76e-07***

(4.43e-08)

Remittance Transfer/

 Neighborhood Income

-0.004

(0.012)

Observations 246,968 246,968 245,074 244,987

R-squared 0.365 0.367 0.366 0.367

Notes: Standard errors are in parentheses. Statistical significance is indicated by asterisks 

where *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively. All 

estimations include a fixed effect for each combination of state and VantageScore. 

Coefficients for “other race” and “multi race” are omitted to conserve on space.

TABLE B2: EXPANDED ESTIMATIONS OF CREDIT PERFORMANCE FROM JANUARY 2009 TO DECEMBER 2010