Finance and Economics Discussion Series
Divisions of Research & Statistics and Monetary Affairs
Federal Reserve Board, Washington, D.C.

Credit Where None Is Due? Authorized User Account Status and ”Piggybacking Credit”

Robert B. Avery, Kenneth P. Brevoort, and Glenn B. Canner
2010-23

NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary
materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth
are those of the authors and do not indicate concurrence by other members of the research staff or the
Board of Governors. References in publications to the Finance and Economics Discussion Series (other than
acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

Credit Where None is Due? Authorized User Account Status and “Piggybacking Credit”
Robert B. Avery
Kenneth P. Brevoort
Glenn B. Canner*

March 5, 2010

Abstract:

An “authorized user” is a person who is permitted by a revolving account holder to use
an account without being legally liable for any charges incurred. The Federal Reserve’s Regulation
B, which implements the 1974 Equal Credit Opportunity Act, requires that information on spousal
authorized user accounts be reported to the credit bureaus and considered when lenders evaluate
credit history. Since creditors generally furnish to the credit bureaus information on all authorized
user accounts, without indicating which are spouses and which are not, credit scoring modelers
cannot distinguish spousal from non‐spousal authorized user accounts. This effectively requires
that all authorized user accounts receive similar treatment. Consequently, becoming an authorized
user on an old account with a good payment history, may improve an individual’s credit score,
potentially increasing access to credit or reducing borrowing costs. As a result, the practice of
“piggybacking credit” has developed. In a piggybacking arrangement, an individual pays a fee to be
added as an authorized user on an account to “rent” the account’s credit history. This paper
provides the first comprehensive look at authorized user accounts in individual credit records and
how their importance differs across demographic groups. Our analysis suggests that piggybacking
credit can materially improve credit scores, particularly for individuals with thin or short credit
histories. We also evaluate the effect that eliminating authorized user accounts from credit scoring
models would have on individual credit scores. Our results suggest that removing this information
has relatively little effect on credit scores, but may reduce model predictiveness.

* Board of Governors of the Federal Reserve System, Washington, DC. Email addresses: ravery@frb.gov,
kenneth.p.brevoort@frb.gov, and gcanner@frb.gov. The opinions expressed in this paper are those of the
authors and do not necessarily reflect the views of the Federal Reserve Board or its staff. We thank Ezra
Becker, Leonard Chanin, Jane Gell, Catherine Henderson, Robin Prager, David Stein, and Chet Wiermanski for
helpful comments, Cheryl Cooper for research assistance, and Rick McKinney for help assembling a history of
Regulation B. Any remaining errors are our own. Ken Brevoort also thanks the Credit Research Centre and
Business School at the University of Edinburgh for their hospitality while working on this paper.

 

Introduction

Revolving account holders, such as credit card users, may designate other individuals as
“authorized users” on their accounts. An authorized user is a person who is permitted to use an
account without being legally liable for any charges incurred.
When an authorized user on an account is the spouse of an account holder, the Federal
Reserve Board’s Regulation B (“Reg. B”), which implements the 1974 Equal Credit Opportunity Act
(ECOA), imposes two important requirements on creditors. First, when providing information to
the credit bureaus, creditors are required to furnish information for the authorized user as well as
for the account holders. Second, when using credit history to assess the creditworthiness of
applicants, creditors are required to consider, when available, the history of accounts held by the
applicant’s spouse on which the applicant is an authorized user (as well as those accounts that are
jointly held).1 These requirements have been in place since Reg. B’s inception in 1975.
In promulgating these provisions of Reg. B, the Federal Reserve Board pointed to
complaints received from women who were unable to obtain credit because information on
accounts jointly held with their husbands was reported to the credit bureaus in the husband’s name
alone. Additionally, the Board took the view that, since some state laws hold one spouse liable for
debts incurred by the other, a spouse should have the “benefit or burden” of the credit history of
their spouse’s accounts that they were authorized to use. Further motivation was provided by the
significant role that spousal authorized users were found to play in the maintenance of an account,
such that the payment history on an account was often “as much the product of the user’s
contribution as that of the obligor.”2
In addition to helping spousal authorized users build an independent credit history,
granting authorized user status has been used to help young individuals learn to manage credit and
build a credit history. This is possible because creditors generally have followed a practice of
furnishing to credit bureaus information about all authorized users, whether or not the authorized
user is a spouse, without indicating which authorized users are spouses and which are not. This
practice does not violate Reg. B.
As a result, the information maintained in credit bureau records generally does not
distinguish spousal from non‐spousal authorized users. This prevents credit scoring modelers and

1 See Section 202.6(b) of Regulation B (12 CFR 202.6(b)).
2 A discussion of the motivation behind the provisions of Regulation B can be found in the
accompanying notice of final rulemaking in 40 Federal Register 205 (22 October 1975), pp. 49298‐49310.

 

creditors that use credit reports from distinguishing spousal from non‐spousal authorized user
accounts. Since spousal authorized user tradelines must be considered in evaluating
creditworthiness to comply with the requirements of Reg. B, but may not be identifiable in an
applicant’s credit record, creditors may have to consider all authorized user accounts on an
individual’s credit record, regardless of whether they reflect a spousal relationship to an account
holder. For this reason, credit history scores, such as the FICO score,3 have traditionally accorded
authorized user accounts equal weight to the other accounts on an individual’s credit record.
The practices described above have the unintended consequence of creating the
opportunity for “piggybacking” credit to emerge. Piggybacking occurs when an individual becomes
an authorized user on an account for the sole purpose of improving that person’s credit history.
Because of the manner in which authorized user information is reported to the credit bureaus, the
full credit history of an account is reflected on the credit records of both an account holder and an
authorized user, regardless of when the authorized user was added to the account. Consequently, a
person’s credit report may reflect several years of account history as soon as that person becomes
an authorized user. If the account has desirable characteristics (such as a low utilization rate or a
good payment history), this may improve the authorized user’s credit risk profile and credit scores.
The result may be enhanced access to credit and reduced borrowing costs.
Beginning in 2007, companies began to emerge to help borrowers with poor credit histories
piggyback on the good credit history of others. Individuals pay a fee to these companies to locate
an account holder who is willing to add this person to their account in exchange for a portion of the
fee.4 The person added to the account is an authorized user in name only, as the individual receives
neither the account number nor an access device (such as a credit card) and consequently cannot
use the account for purchases.5 By piggybacking on someone else’s account history, however, an
authorized user may be able to improve their credit score in advance of a credit application,

3 FICO scores are a trademark of Fair Isaac Corporation. For more information on the FICO score,
refer to http://www.myfico.com.
4 Industry sources indicate that individuals pay between $1,000 and $2,000 to obtain an authorized
user account and that the individual renting out an account can earn about $200 per month. Refer to Harney
(2007), Yuille (2007), and Berney (2007).
5 This approach is not without risks to the account holder. If the person added as an authorized user
is able to obtain an access device directly from the lender, then they are legally permitted to run up charges
on the account.

 

potentially resulting in lower borrowing costs or an ability to qualify for credit that otherwise
would not be extended.
The practice of piggybacking credit has raised concerns that the credit scores of people with
authorized user accounts may not accurately reflect their creditworthiness. For example, a study
by Fitch Ratings (Pendley, Costello, and Kelsch, 2007) of mortgage defaults points to the presence of
authorized user accounts on the credit records of high‐FICO borrowers who defaulted on their
loans as evidence of poor underwriting practice by the lender. In response to concerns about the
role of authorized user accounts in credit scores, Fair Isaac, which has traditionally treated
authorized users and account holders identically, has revised the FICO credit scoring model to place
less weight on those accounts on which an individual is an authorized user. Despite these concerns,
very little is known about the role played by authorized user accounts in credit history files and, to
date, the Federal Trade Commission has not taken action against companies that offer piggybacking
arrangements.
This study examines the role of authorized user accounts in credit scoring, with particular
focus on the role of authorized user accounts in the credit records of married women, who whose
treatment was a central motivating factor in the requirements of Reg. B, and consequences of
piggybacking credit. We rely on a unique, nationally representative sample of credit records and a
credit scoring model (the “FRB base model”) constructed by staff of the Federal Reserve Board as
part of a study of credit scoring mandated by Congress. Our analysis does three things. First, we
provide a detailed profile of authorized users examining how the use of authorized user status
differs across race or ethnicity, age, sex, and marital status in terms of its prevalence and
importance to an individual’s credit record. Using the FRB base model, we examine the
contributions that authorized user accounts make to the credit scores of individuals in our sample.
Particular focus is placed on the effect of authorized user accounts on the credit scores of married
individuals.
Second, we evaluate the size of the potential gains an individual can achieve as a result of
piggybacking credit. Using the FRB base model, we simulate the effect on an individual’s credit
score of being added as an authorized user to an established account with a good payment history.
This allows us to identify the population whose scores are most likely to be affected by purchasing
authorized user status and to estimate the potential harm that results from this practice. Our
analysis reveals that these benefits are potentially quite large (particularly for people with thin or
short credit histories), suggesting that the practice of piggybacking credit, in some cases, may
artificially enhance an individual’s access to credit without improving the individual’s true
creditworthiness.
Finally, we examine how credit scores change when authorized user accounts are not used
in the construction and application of credit scoring models. This is done by re‐estimating the FRB
base model, using credit characteristics that exclude information from authorized user accounts,
and generating scores based on this new model. The results of this analysis suggest there would be
little impact on credit scores if a credit scoring model did not incorporate information on
authorized user accounts. Nevertheless, the elimination of authorized user accounts slightly
reduces the predictiveness of the FRB base model.
The remainder of the study documents the details of our analysis. The next section outlines
the data used in this study and provides additional information on the development of the FRB base
model. In the following section, we profile authorized user accounts and describe how FRB base
scores are affected by the presence of this account information. In the two subsequent sections, we
then describe how credit scores and model predictiveness are altered by the exclusion of
authorized user trades and simulate the potential impact of piggybacking credit on individual credit
scores. The final section summarizes our findings and discusses the public policy implications.

The Data and FRB Base Model

This study uses a large, nationally representative sample of individual credit records that has been
augmented with personal demographic information on each individual. This dataset was
assembled by staff of the Federal Reserve Board for use in its Report to Congress on Credit Scoring
and its Effects on the Availability and Affordability of Credit (Board of Governors, 2007) and, as of
this writing, is the only nationally representative dataset of its kind, since credit records do not
include any personal demographic information, other than age (which is frequently missing).6 The
following briefly outlines the contents of this dataset.7
The dataset contains a nationally representative random sample of approximately 300,000
credit records from TransUnion, LLC, as of June 30, 2003 and updated as of December 31, 2004.
These records include information on each individual’s credit accounts or “tradelines,” collection

6 This study was requested by Congress in Section 215 of the Fair and Accurate Credit Transaction
Act, Public Law 108‐159, enacted December 4, 2003. The study is available at
http://www.federalreserve.gov/pubs/reports_other.htm.
7For more detail on the contents of this database, see Board of Governors (2007) or Avery, Brevoort,
and Canner (2009).

 

accounts, monetary‐related public records, and a complete record of inquiries made by creditors
and others legally entitled to the information.8 These data indicate whether a person individually
holds, jointly holds, or is an authorized user on each of the tradelines in her credit record. The
dataset also includes two commercially available credit scores, the TransRisk Account Management
Score (“TransRisk score”) and the VantageScore.9
The information provided by TransUnion included a file of 312 precalculated variables,
referred to as “credit characteristics.” These credit characteristics are summary measures of each
credit record (for example, the age of an individual’s oldest account, or the total number of serious
delinquencies) and were created by TransUnion to facilitate the development of credit scoring
models for themselves and for their customers. A subset of these credit characteristics comprise
the inputs into the FRB base model.
As noted, the only personal demographic information included in credit records is date of
birth (though this information was missing in about one‐third of the credit records). However,
credit records contain additional types of information (such as name, Social Security number, and
current and previous addresses) that were used to obtain demographic information on each
individual from the U.S. Social Security Administration (SSA) and from one of the nation’s leading
demographic information companies.10
The SSA collects demographic information when individuals apply for a Social Security
card.11 With the names and Social Security numbers provided by TransUnion, the SSA provided
information on each individual including their citizenship, the date they filed for a Social Security
card, their place of birth (state or country), race or ethnic description, sex, and date of birth. An

8 A detailed assessment of the contents of credit records is provided by Avery, et al. (2003).
9 Approximately 23 percent of the sample population was unscoreable by at least one of the credit
scoring models (the vast majority of whom were scoreable by neither). Generally, an individual credit record
is unscoreable when it does not contain a sufficient amount of recent account activity.
10 The matches involved a double‐blind process between TransUnion and the other data providers so
that the integrity and privacy of each party’s records were maintained. No individually identifying
information was provided to the Federal Reserve Board, no credit history information was received by the
SSA or demographic company, and no demographic information was provided to TransUnion. The
demographic information company has elected to remain anonymous.
11 The application form for a Social Security card is SS‐5 (05‐2006). This form can be viewed online
at http://www.ssa.gov/online/ss‐5.html.

 

applicant for a Social Security card is required to supply all of this demographic information as part
of their application, with the exception of race or ethnicity, which is requested by not required.12
Information on each individual’s marital status was provided by the demographic
information company. The company culls this information from thousands of public and private
data sources and supplies it to creditors and other interested entities for use in marketing and
solicitation activities.
As mentioned earlier, this study also makes use of the FRB base model, a credit history
score developed by staff of the Federal Reserve. This credit scoring model was constructed using a
model‐building algorithm that was designed to mimic the process used by industry model‐builders.
The FRB base model was estimated using the same sample of credit records used in this study.
The FRB base model is comprised of three different scorecards. Individuals with two or
fewer tradelines in their credit records are scored on the “thin” scorecard. People with more than
two tradelines are placed on the “dirty” scorecard if they meet any of the following three
conditions: (1) they have been 90 or more days past due on an account; (2) they have a monetaryrelated
public record on their file (such as bankruptcy or garnishment); or (3) they have collection
accounts totaling more than $50. Other individuals are placed on the “clean” scorecard. A separate
equation, with a separate selection of right‐hand‐side credit characteristics, is used for each of these
scorecards.
The model for each scorecard is designed to predict whether a person will have “bad” or
“good” performance, measured as each individual’s worst performance on a new or existing
account during an 18 month performance window of July 2003 through December 2004. 13
Accounts that were 90 or more days past due exhibit “bad” performance and accounts that were not
30 or more days past due, but that have evidence of payments having been made, had “good”
performance (other accounts had “indeterminate” performance). An individual’s performance is
“bad” if one or more of his accounts had bad performance during the performance period.
Conversely, an individual’s performance was “good” if he had good performance on at least one
account without any accounts with bad or indeterminate performance. Otherwise, an individual’s

12 For further discussion about the SSA data and how they were matched to the credit records, see
Board of Governors (2007).
13 New accounts are those that were opened during the first 6 months of the 18 month performance
period. Existing accounts were those that were open as of June 2003 with no evidence of delinquency. This is
a common definition of credit performance used by credit scoring model builders.

 

performance is classified as “indeterminate.” The dependent variable in these estimations is a
dichotomous indicator variable that takes on a value of 1 for good performance and 0 for bad
performance. Individuals who have indeterminate performance are excluded from the estimation
of the model, but are used in evaluating the model’s effectiveness.
A separate linear probability model was estimated for each of the three scorecards.14 The
credit characteristics that comprised each model were selected using a forward stepwise selection
process using the 312 credit characteristics supplied by TransUnion. Each credit characteristic
entered the model as a step‐function (or series of “credit attributes”), where the breakpoints of the
individual steps were selected using statistical criteria used by industry model builders.
Characteristics were selected until the marginal improvement in the divergence statistic from the
addition of another characteristic fell below a specified threshold. This process was designed to
mimic the process used by industry model builders (for example, each credit characteristic enters
the model as a step function and the coefficients assigned to the steps of a characteristic, or the
“credit attributes,” were constrained to be monotonic). A detailed discussion of the model building
process is provided by Board of Governors (2007).15
The fitted values from these estimated equations are then normalized to a rank‐order scale.
The normalization is constructed such that an individual’s score represents the percentile of the
distribution into which that score falls. In other words, 25 percent of the individuals in the sample
had a score of 25 or less. These normalized scores range between 0 and 100.16 Using two
commercially available credit scores (the TransRisk Account Management score from TransUnion
and the VantageScore) that had been normalized to an identical rank order scale, Board of
Governors (2007) finds that the credit score patterns across demographic groups were very similar

14 The use of a linear probability model in the FRB base model was motivated by concerns about the
additional computational burden of estimating a probit or logit model. Avery, Brevoort, and Canner (2009)
use the same model‐building process as the FRB base model but replace the linear probability model with a
logit and find almost identical results to Board of Governors (2007).
15 In some ways the process deviated from that used by industry model builders. Most importantly,
this process was entirely algorithmic, whereas commercial model building generally involves greater use of
art (e.g., modeler experience and intuition). Additionally, the FRB base model was constructed using a linear
probability model, rather than the more commonly used logit model. Avery, Brevoort, and Canner (2009)
conduct a similar analysis to that of Board of Governors (2007) using a logit model and find that the results
are qualitatively identical.
16As a frame of reference, a single point in the FRB base model is roughly equivalent to five points on
a FICO or VantageScore scale.

 

across the three credit scoring models. This suggests that the FRB base model closely approximates
score differences across demographic groups observed in commercially available models.

Profile of Authorized User Accounts

This analysis focuses on the treatment of authorized user tradelines in credit scoring models and,
consequently, we focus on the “scoreable” population, as defined by the FRB base model. In
general, an individual credit record is considered to be “unscoreable” when it lacks sufficient
information (such as information on at least one tradeline) or when there is no evidence of recent
account activity.17 Excluding the unscoreable population, for whom the treatment of authorized
user tradelines in a credit scoring model is largely irrelevant, leaves a scoreable population of
232,467 credit records, all of which have at least one non‐authorized user tradeline.
Authorized user accounts are not rare. As shown in table 1, over one‐third of the scoreable
population had one or more authorized user tradelines. Only a small fraction (1.3 percent),
however, had more authorized user tradelines than non‐authorized user tradelines (that is,
tradelines on which the individual is an individual or joint account holder).18 Across racial or ethnic
groups, authorized user status was found more frequently among non‐Hispanic whites than other
groups, particularly blacks, only 19.9 percent of whom had authorized user accounts on their credit
records. Authorized user accounts are also observed more frequently for married individuals than

17 The technical definition of a scoreable credit record for the FRB base model is that the record
should have had both a TransRisk score and a VantageScore. This definition was largely equivalent to
requiring each individual be scoreable under the TransRisk scoring model, as only 39 out of 301,536 records
had a VantageScore but no TransRisk score. Approximately 23 percent of the sample population was
unscoreable by at least one of the credit scoring models. Generally, an individual credit record is unscoreable
when it does not contain a sufficient amount of recent account activity. For a detailed examination of the
contents of the unscoreable credit records, see Board of Governors (2007).
18 One reason this number is so small is that a credit record needs at least one non‐authorized user
tradeline to be scoreable. Consequently, credit records with only authorized user tradelines are excluded
from the scoreable sample.
The term “non‐authorized user tradeline” in this paper means that the individual whose credit
record is being examined is not an authorized user on the account. This does not imply that there are no
authorized users on that account. For example, if an account has a single account holder and an authorized
user, that account information (which is reported on the credit records of both individuals) is considered an
authorized user account for the authorized user and as a non‐authorized user account for the account holder.

 

single individuals and less frequently for individuals under 30 years of age than for older
individuals.
Table 1 also provides a detailed breakdown of the share of scoreable individuals with
authorized user accounts that is based upon the contents of individual credit records. Generally,
authorized user accounts are more likely to be found in credit records that are “thicker” (that is,
that have more tradelines), older, or that have no evidence of delinquencies in the past 24 months.
Overall there appears to be little relationship between credit card utilization and authorized user
status.
Table 1 also indicates that across demographic and credit record groups, individuals with
authorized user tradelines had higher VantageScores than individuals without authorized user
accounts. Since the VantageScore does not use information on authorized user accounts, this
difference reflects entirely differences in each individual’s non‐authorized user accounts. As shown
in table 2, authorized user tradelines are generally higher quality than non‐authorized user
tradelines, in that they tend to be older, have lower utilization rates, and slightly lower delinquency
rates. The differences between authorized user and non‐authorized‐user tradelines appear to hold
for most demographic and credit record groups. Nevertheless, the characteristics of authorized
user and non‐authorized‐user tradelines appear to be very similar across groups. For example,
individuals under the age of 30 have younger tradeline ages for both their authorized user and nonauthorized
user accounts than do older individuals and people with more delinquencies on their
non‐authorized user accounts have higher delinquency rates on their authorized user accounts as
well.
Particularly interesting from the standpoint of Reg. B is the difference in the presence of
authorized user tradelines of the credit records of married men and women. As shown in table 1,
the share of married women in the sample with authorized user tradelines was over 10 percentage
points higher than the share of married men. Additionally, married women were twice as likely as
married men to have authorized user tradelines constitute the majority of the tradelines on their
credit record. These differences are much more pronounced than the differences between single
women and men.
While these numbers appear consistent with the concerns originally used to motivate the
authorized user provisions of Reg. B in the 1970s – that the credit accounts of married individuals
tended to be reported in the husband’s name – a closer look at the data suggests that these
concerns may be less relevant today. While married women have more authorized user tradelines
on their credit records than do married men (1.26 versus 0.90), there is only a slight difference
between married men and married women in the number of non‐authorized user tradelines on
their credit record. Married women have, on average, 16.69 non‐authorized user tradelines and
married men have 16.72, a statistically insignificant difference. When the comparison is limited to
open accounts, married women on average have more authorized user and non‐authorized user
accounts than married men.19 This is consistent with the numbers provided in table 2, which show
that married women have more authorized user and non‐authorized user credit card tradelines
than do married men. This analysis of the credit bureau record contents provides little evidence
that the credit records of married women are less complete than those of married men.

Effects of Authorized User Accounts on Credit Scores and Access to Credit across Populations

To examine the contribution that the presence of authorized user accounts on an individual’s credit
record make to credit scores (in credit scoring models such as the FICO score that include them)
and, consequently, access to credit for different populations, each of the credit characteristics that
comprise the FRB base model was recalculated without the authorized user accounts. A new value
for the FRB base score was computed based upon these recalculated credit characteristics. This
new value represents the credit score that each individual would have received had their
authorized user accounts not been in their credit record. The difference between each individual’s
FRB base score and this new value measures the contribution the authorized user accounts to each
individual’s credit score.
Table 3 provides the mean change in the FRB base score by demographic group resulting
from the inclusion of authorized user accounts. A positive value indicates that an individual’s score
is higher when her authorized user accounts are factored into the calculation of the score than it
would have been had these account not been reflected in her credit report. For individuals without
authorized user accounts on their credit record, the score change will equal zero by definition.
Therefore, the means presented in table 3 are calculated using only those individuals in each
demographic group who have authorized user tradelines in their credit records. These changes are

19 An account is considered to be open if it has not been reported as closed, has no comment codes indicating
that it has been closed, and was verified after February 2003.

 

best interpreted as marginal contributions to an individual’s credit score made by authorized user
accounts and not as the changes that would result if credit scoring models were prohibited from
using authorized user accounts, which is an exercise we conduct later in the paper.
Overall, the data suggest that people with authorized user accounts on their credit records
experience an average score increase of 0.49 points and a median change of zero points because of
the inclusion of this information in the score. Across different demographic groups, authorized
user accounts appear to contribute very little to credit scores. The median score change for each of
the demographic groups was zero, and the means, which were generally positive, were generally
lower than 1. This suggests that none of the demographic groups examined appear to be
substantially benefited or harmed, on average, by the authorized user tradelines in their credit
records. For most demographic groups, a majority of individuals experienced score changes
(positive or negative) of 2.5 points or less from the authorized user accounts on their credit record.
As discussed in the previous section, there are significant differences between married men
and women in the number of authorized user tradelines in their credit records. These differences
are reflected in their credit scores, as the increase in mean credit scores for married women (0.56
points) is larger than for married men (0.20). As with the other demographic groups, these
differences are relatively small and suggest that the presence of authorized user tradelines can
explain only slightly more than 10 percent of the average score difference between married women
and men.20 These results suggest that the credit scores of married women are marginally higher
than married men, even without contribution made by authorized user tradelines.
Besides demographic groups, there may be other subsets of the population that are more
likely to be helped or harmed by the presence of authorized user accounts. In particular, the
importance to an individual of authorized user accounts in the calculation of a credit score may
differ substantially depending upon the contents of an individual’s credit record. In particular,
those individuals with fewer non‐authorized user accounts on their file, shorter credit histories, or
higher utilization rates on their non‐authorized user tradelines may stand to gain (or lose) the most
from the inclusion of authorized user accounts in the calculation of a credit score. In contrast, those
individuals with a large number of accounts or who are currently delinquent on one or more

20 The mean FRB base score for married women is 3.3 points higher than for married men. Removing
authorized user tradelines decreases the mean scores of married women by about 0.36 points more than
married men. This suggests that the presence of authorized user account information in the FRB base model
can explain 0.36/3.3 = 10.9 percent of the difference in credit scores between married men and women.

 

accounts may be the least susceptible to benefit or harm from the inclusion of authorized user
tradelines.
To examine this possibility, table 3 also shows score changes for different groups based
upon the contents of the each individual’s credit record. The score changes suggest that some of the
groups that were identified as being more likely to benefit from the inclusion of authorized user
accounts experienced larger gains than other groups. In particular, those people with thin credit
records (2 or fewer non‐authorized user tradelines) or short credit histories (oldest non‐authorized
user account less than 24 months old) both experienced increases in credit scores of approximately
5 points on average because of authorized user accounts. This may reflect, in part, that these two
groups have the highest share of individuals for whom authorized user accounts constitute at least
half of the tradelines in their credit records (see table 1). In contrast there was a less consistent
pattern of benefit or harm based upon past payment performance or utilization rates.21
On the whole, this analysis suggests that the inclusion of authorized user account
information in computing an individual’s credit score has only a modest effect. Furthermore, this
inclusion does not appear to have a disproportionate impact on the members of any particular
demographic group. However, there are some small subsets of the population (in particular,
individuals with very thin or very short credit histories) for whom the inclusion of authorized user
accounts has a relatively larger effect on scores. Consistent with our earlier findings about the
relative quality of authorized user and non‐authorized user tradelines, score changes from the
inclusion of authorized user tradelines are generally positive.

The Effect of Piggybacking Credit on Credit Scores

The modest contribution that authorized user accounts appear to make, on average, to individual
credit scores does not suggest that the potential change that can be achieved by buying authorized
user status on an account is necessarily small. While authorized user accounts are generally higher quality
than non‐authorized user accounts (in that they have lower utilization rates, older ages, and
better payment histories) within most groups examined, the pattern of authorized user and nonauthorized
user accounts across groups is similar. For example, both the authorized user and non‐

21 There are two minor exceptions to this in table 3. Individuals with no observed performance on
their accounts see their scores increase by 6.8 points and individuals with no revolving accounts on which
utilization can be calculated experience increases of 2.8 points. Both of these groups tend to have very thin
files.

 

authorized user accounts for young individuals (under age 30) are not as old on average, as the
accounts of older individuals. Consequently, the estimates from the previous section may
underestimate the gains in score that can be achieved from piggybacking on an even higher‐quality
account.
To evaluate the potential credit score boost that an individual might achieve by
piggybacking on an additional high‐quality account, we use the FRB base model to simulate the
effect that the addition of an authorized user account would have on each individual’s credit score.
This process helps measure the magnitude that such changes are likely to have and helps identify
which groups of individuals are the most likely to benefit from piggybacking credit.
The purpose of this exercise is to approximate the potential scope of the problem that
piggybacking credit represents. If piggybacking credit has little or no impact on credit scores, or if
the benefits are limited to a very small segment of the population, then the need for a remedy will
be relatively smaller. In contrast, if piggybacking credit can increase credit scores substantially for
a large share of the population, then the potential for harm from piggybacking credit may warrant a
reconsideration of existing regulations, industry practices, or both to preserve the predictiveness of
credit scoring models.
The simulation begins with the credit records of each of the 232,467 individuals in the 2003
sample and adds to their credit record an additional, authorized user credit card account. To
provide an estimate of the maximum amount of benefit one could get by adding an authorized user
tradeline to their record, we use values from the 90th percentile of the distributions for account age
and credit limit (which translate into an account opening date of March 1987 and a credit limit of
$15,000) and assume that the account has an unblemished payment history since opening.22
Additionally, we assume that the current balance on the account is $1. We simulate what each
borrower’s FRB base score would have been after the addition of this account and compare this to
the original FRB base score. The increase in score that results from the addition of this simulated
authorized user account provides an estimate of the benefits to a consumer of piggybacking credit.
Table 4 shows a breakdown of score changes by the demographic characteristics and credit
record classifications used earlier. On average, the addition of this simulated account increases
credit scores by 6.9 points. Those segments of the population that were identified earlier as being

22 Carson and Becker (2007, p. 2) report that the intermediaries who facilitate piggybacking
generally seek out accounts with ages ranging from “two years to decades” and that the credit limits on these
accounts often exceed $50,000.

 

the most likely to benefit from an ability to purchase authorized user status, however, experienced
much larger increases. The largest increase in score was experienced by individuals whose oldest
tradeline was less than 2 years old. The addition of this simulated tradeline increased the credit
scores for this group by an average of 22.4 points over the starting mean score of 37.9. As expected,
individuals with thin credit files (2 or fewer non‐authorized user tradelines) also experienced large
increases in score, with their scores rising on average from 44.6 to 64.0.23
The importance of a change in credit score will depend crucially upon both the size of the
change and the initial credit score. For example, a 20 point score increase might have a smaller
effect for an already prime‐quality borrower (who may already qualify for credit on the most
favorable terms) than it would for a subprime borrower, who as a result might now appear to be
near‐prime or even prime‐quality. To provide a better understanding of how credit score changes
experienced after the addition of the simulated AU tradeline vary with the starting FRB base score
level, figure 1 shows the mean change in score that resulted from the simulation against the
beginning FRB base score.
As seen in that figure, there is substantial variation in the mean score change by credit score
level. The smallest mean increases (and only decreases) are observed for individuals at the highest
credit score levels. These are people who already are identified by the credit scoring model as
having low default probabilities, such that the additional information provided by the simulated
tradeline has little or no beneficial effect. The remainder of the population experienced larger
increases on average, with a noticeable dip in the low 20s. The bottom of this dip occurs at a score

23 The score changes summarized by table 4 also show that a small number of people (representing
about 1 percent of the total population) experience a score decline following the addition of the simulated AU
account to their credit record. While it may surprise some that a credit score can be reduced by adding
information on an unambiguously “good” account, this result is not wholly unexpected. The reason is that
while the simulated account will have increased or left unchanged the score of any individual who remained
on the same scorecard, when the addition of an account alters the scorecard with which an individual is
scored, unintuitive results can arise.
In the FRB base model, which contains only three scorecards, the addition of the simulated account
can have the effect of moving people who would have been scored on the thin scorecard, without the
simulated account, onto either the clean or dirty scorecards. People who were initially scored on the clean or
dirty scorecards would not have changed scorecards. Because of this movement between scorecards, some
individuals (particularly those who moved from the thin to the dirty scorecard) saw their credit scores
decline as their profile appeared worse when evaluated using the model on their new scorecard. Since most
credit scoring models make use of more than 3 scorecards (for example, the VantageScore is comprised of 12
different scorecards) this suggests that the FRB base model may understate the share of individuals who
change scorecards as a result of the additional authorized user tradeline and who therefore may be subject to
such counterintuitive score changes. For more information on the 12 scorecards that comprise the
VantageScore, see VantageScore (2006).

 

of approximately 24, which roughly corresponds to the boundary between subprime and nearprime
credit scores.24
An alternative method of evaluating the impact of the addition of the simulated tradeline is
to look at threshold effects. In this case, we examine how many people with subprime‐level credit
scores experienced score increases that moved them into near‐prime or even prime levels. The
results of this analysis suggest that there is substantial potential for movement across these credit
risk categories (table 5). More than one‐quarter of the 56,000 subprime borrowers in our sample
experienced credit score increases that moved their credit score into the near‐prime range because
of the simulated tradeline. Similarly, over one‐third of near‐prime borrowers had prime credit
scores because of the simulated tradeline and 1.2 percent of these borrowers improved to superprime
credit scores.
While there are only small differences across demographic groups, the credit record
population segments that we have already highlighted exhibit much larger threshold effects.
Amongst the thin file population, for example, 46.8 percent of subprime borrowers are moved into
the near‐prime segment and an additional 3.6 percent become prime borrowers because of the
simulated tradeline. Threshold effects are notably smaller, however, for individuals with past
delinquency. For example, only 7.9 percent of subprime borrowers with two or more delinquencies
in the past 24 months experience score increases into the near‐prime segment and only 3.4 percent
of near‐prime borrowers with 2 delinquencies receive prime scores.
These numbers appear to indicate that the practice of piggybacking credit can increase
credit scores to an economically‐significant extent, if the account to which a non‐prime borrower is
being added is of sufficiently high quality. Furthermore, it appears that a large fraction of
borrowers – particularly borrowers with thin or short credit histories – can obtain substantially
higher credit scores as a result of this practice. This suggests that the practice of piggybacking
credit offers substantial potential to increase the credit scores of individuals added as authorized
users on existing accounts and consequently to enhance their access to credit at lower costs.

24 The boundaries used to delineate subprime, near‐prime, prime, and super‐prime credit scores in
this study are based on the VantageScore cutoffs for these groups (VantageScore, 2008) applied to the
normalized 0‐100 scale used for the FRB base model.

 

Removing Authorized User Accounts from Model Construction

We next consider the potential effect of eliminating consideration of authorized user tradelines in
credit scoring models on the credit scores of individual borrowers (and hence their ability to access
consumer credit) and the accuracy of credit scoring models, which may hinder the ability of
consumer credit markets to allocate credit efficiently. Some credit scoring models in use today,
including the VantageScore, exclude authorized user tradelines from their models and others, such
as the FICO score, have altered the way authorized user tradelines are treated by their models.
These policies, whether taken in response to abuse from piggybacking credit or not, can affect not
only the credit scores of individual borrowers (and hence their ability to access consumer credit
markets), but also the accuracy of credit scoring models, which may hinder the ability of consumer
credit markets to allocate credit efficiently. In this section, we evaluate how removing authorized
user accounts from credit score model construction affects both the credit scores of borrowers and
the predictiveness of credit scoring models.
While the earlier analysis documented the contribution that authorized user accounts make
to an individual’s credit score, the results are not fully indicative of the effect that removing
authorized user accounts from a credit scoring model would have on individual credit scores. This
is because in the earlier exercise, we removed the authorized user tradelines but left the credit
scoring model itself unchanged. If, however, authorized user tradelines were systematically
excluded from credit scoring models, model builders would be expected to recalibrate their models
to reflect this. The result would be a different credit scoring model that was optimized to predict
future credit performance without using authorized user tradelines.
In this section, we examine how credit scores would change if authorized user tradelines
were systematically excluded from the construction and application of credit scoring models. Using
the credit characteristics that were calculated without authorized user accounts, we re‐estimate the
models for each of the three credit scorecards that comprise the FRB base model.25 Fitted values
from these new models are then normalized to the same rank‐order scale used for the FRB base
model (however, the mapping between fitted values and credit score differs from that of the FRB
base model). This new score is then compared to the original FRB base score for each individual.

25As part of the process of re‐estimating each scorecard model, we recalculate the attributes
constructed for each credit characteristic. This is done using the same algorithm used in generating the
original FRB base model. For more information on the process used to construct attributes, see Board of
Governors (2007).

 

The result of this process is an approximation of the credit scoring model that would have resulted
had authorized user tradelines not been used in model development.26 As such, it represents the
outcome that would result if model developers excluded authorized user tradelines from their
models.
Table 6 shows how credit scores change for each population group when the credit scoring
model is re‐estimated without authorized user accounts. The re‐estimation of the credit scoring
model will impact scores for almost all individuals, but because the effects of these changes are
expected to be much larger for individuals with authorized user tradelines, we again focus on this
subset of the population. The results indicate that the reestimated scoring model produces scores
that are only 0.30 points lower, on average, for authorized users than the FRB base model. None of
the demographic groups experience a score decline of more than 1 point. While the score decline
was somewhat larger for married women than married men, credit scores remained higher for
married women.
Across credit record groups, again it is the populations with thin or short credit histories
that experience the largest declines. Mean scores for the thin file population and for the individuals
whose oldest non‐authorized‐user tradeline was less than 2 years old both fell by 5.21 and 4.35
points respectively. These declines are somewhat lower than what would have been implied by our
earlier exercise of removing authorized user account information while leaving the underlying
scoring model unchanged. This may suggest that the within‐group similarities in the characteristics
of authorized user and non‐authorized‐user tradelines help capture some of the information
provided by authorized user tradelines.

Evaluating the Predictiveness of Authorized User Account Information

We next consider what impact including, or alternatively excluding, authorized user account
information in credit scoring models will have on the model’s ability to predictive future credit
performance. Since authorized users are not liable for any debt incurred, performance on these
accounts may not reflect the authorized user’s creditworthiness and consequently may have little
relationship to the performance of authorized users on their own (non‐authorized user) accounts

26This approach is an approximation because we use the same set of credit characteristics on each
scorecard as was used in the FRB base model. Reselecting credit characteristics would have required us to
reverse engineer over 300 credit characteristics, a process that would have taken a prohibitive amount of
time.

 

going forward. In such cases, including authorized user accounts in a credit score might reduce the
predictiveness of a credit scoring model. However, if there is a close financial relationship between
the account holder and the authorized user, in that the authorized user may rely on the account
holder to provide financial support and to be a source of financial strength or may be the person
who manages a household’s finances, then both the account holder and the authorized user may
have similar future loan performance. In this case, including authorized user accounts in the
calculation of a credit score should increase the model’s predictiveness.
To assess each model’s predictiveness, we rely on credit performance on non‐authorized
user accounts that were opened in the six month period after the date for which the scores were
calculated (July to December 2003). Credit performance is evaluated over the eighteen month
performance window, running from July 2003 through December 2004 with performance on each
account categorized as good, bad, or indeterminate using the same criteria described earlier in
discussing model construction. All of an individual’s accounts that exhibit performance are
weighted equally with the sum of the weights summing to 1 for each individual. This is equivalent
to measuring performance by selecting a random account from each credit record, which is a
methodology commonly used in assessing credit scoring models.27
Using this performance measure, we calculate the credit scores produced by the FRB base
model and by the re‐estimated model without authorized user tradelines for the credit bureau
records from June 2003. If authorized user accounts increase the predictiveness of a credit scoring
model, we would expect the goodness‐of‐fit measures for the FRB base model to be higher than
those for the re‐estimated model. We rely on two commonly‐used measures of goodness‐of‐fit to
evaluate model predictiveness: the Kolmogorov‐Smirnow (“KS”) statistic and the divergence
statistic.28
Both goodness‐of‐fit measures suggest that a credit scoring model that incorporates
information on the authorized user accounts in a person’s credit record has greater predictive
power for new non‐authorized user accounts than a model that does not consider this information.
As shown in table 7, both the KS‐statistic and the divergence statistic for the FRB base model (57.1
and 2.52, respectively) were higher than the values of these statistics for the versions of the FRB

27The measure of performance used here is identical to the “modified new account” measure used in
Board of Governors (2007) with the additional restriction that only non‐authorized user accounts are
evaluated.
28For additional information on these statistics and their use in credit scoring, see Mays (2004).

 

base model that was re‐estimated without the information on each individual’s authorized user
accounts (57.0 and 2.51). However, both differences were small, suggesting that the increase in
predictive power offered by authorized user accounts was marginal.
In addition to the overall change in score predictiveness, we can also evaluate how
predictiveness is affected for specific subpopulations. Particularly interesting is the effect of
including authorized user accounts in credit scoring models on predictiveness among individuals
with short or thin credit histories based on their non‐authorized user accounts. Since these are the
individuals for whom non‐authorized user accounts provide the least information, one might expect
the additional information provided by authorized user accounts to be particularly effective at
differentiating risk within these categories. Both goodness‐of‐fit measures suggest that the FRB
base model has significantly greater predictiveness with the authorized user tradelines included
than the model that excludes these tradelines. The improvements in fit for these populations are
generally larger than for the other subpopulations examined.
Taken together, these results suggest that information provided by the authorized user
accounts on an individual’s credit record appear to provide additional information about the
authorized user’s future credit performance. The boost to credit score predictiveness, however,
appears to be mild. Additionally, some of the decline in model predictiveness may be mitigated by
altering the selection of credit characteristics included in the model. Nevertheless, authorized user
account information does seem to add additional predictive power to the FRB base model.

Conclusions and Caveats

Regulation B, which implements the Equal Credit Opportunity Act (ECOA), contains several
requirements about the treatment of spousal authorized user accounts. Among the requirements
are that creditors who report information to credit bureaus must report the information in a
manner that reflects the participation of both spouses and that information on spousal authorized
user accounts must be considered, when available, in a evaluating credit history to assess
creditworthiness.
Our analysis of authorized user tradelines in a random sample of credit records suggests
that over one‐third of individuals have one or more authorized user accounts. The characteristics
of authorized user accounts are generally superior to non‐authorized user accounts in that they
tend to be older, have lower utilization levels, and less evidence of past delinquency. The usage of
authorized user account status appears to differ across demographic groups with minorities, single
individuals, and the young being less likely to have authorized user accounts than the rest of the
population.
Married women, whose treatment was a central factor motivating the provisions of Reg. B,
are more likely to have authorized user accounts on their credit records than are married men. The
greater frequency with which married women are authorized users, however, does not appear to
come at the expense of being an account holder. The credit records of married women also had the
same or slightly more non‐authorized user tradelines than the records of married men. This
suggests that the concerns about a lack of credit history for married women as a result of accounts
being reported to the credit bureaus in the husband’s name that motivated the Reg. B provisions
relating to spousal authorized user accounts may be less relevant today, possibly reflecting the
success of ECOA in equalizing credit opportunity and credit reporting for married women.
Based on the analyses documented in this paper, authorized user accounts appear to
provide only a modest boost on average to individual credit scores in a scoring model that
incorporates authorized user account information. Despite the differences in usage patterns across
groups, there is little evidence that authorized user accounts contribute meaningfully to score
differences across demographic groups. While authorized user accounts increased the credit scores
of married women more than married men, this difference explains only about 10 percent of the
score difference between married women and men. For those individuals with thin or short credit
histories, however, the incorporation of authorized user tradeline information may offer an
economically‐meaningful boost to scores.
Despite the minor differences observed in scores as a result of authorized user accounts,
our results suggest that the practice of piggybacking credit can have a large effect on the scores of
some individuals. Particularly for individuals with short or thin credit histories, the addition of a
high‐quality authorized user account can significantly improve an individual’s credit score. In a
substantial fraction of cases, these improvements are economically meaningful in that an individual
with a subprime credit score can be moved to near‐prime levels, or someone with a near‐prime
score can become prime. If an authorized user account is added in advance of a major credit
transaction, such as a new mortgage, the individual may be able to access credit for which he would
not have otherwise qualified or to obtain credit with much lower borrowing costs.
The potential distortions in credit scores that piggybacking credit may introduce suggest
that a reconsideration of existing regulations, industry practices, or both may be warranted to
preserve the predictiveness of credit scoring models. Our results suggest that eliminating
authorized user accounts from a credit scoring model has only a modest effect on score differences
across groups. Nevertheless, these same results suggest that the predictiveness of the credit
scoring model without authorized user tradelines is somewhat diminished from that of the model
that incorporates authorized user account information. This suggests that authorized user
accounts provide useful information and there may be some downside to excluding this
information from credit scoring models.
There are several caveats that go along with this analysis. First, the analysis here utilizes
the FRB base model, instead of a commercially available credit scoring model. While we would
have preferred to use one or more of the models actually used in credit underwriting, these models
were not available and without the estimation sample used (along with demographic information
on at least marital status) would not have been sufficient. Nevertheless, the FRB base model has
been shown to produce very similar results to commercially available models (Board of Governors,
2007) and we believe that the results obtained from this model are broadly applicable.
Second, our analysis has examined the potential score improvement that an individual can
achieve by piggybacking on a high quality account. The characteristics of the account used in our
simulation were selected to provide, essentially, a reasonable upper bound on the benefit that an
individual could achieve by buying authorized user status. The actual accounts available for
piggybacking may result in smaller score improvements or even score declines (particularly if the
account eventually becomes delinquent).
Third, the analysis presented in this paper has looked at a single possible response to
piggybacking credit. Other possible responses, such as continuing to incorporate authorized user
account information in credit scoring models but in a manner that is distinct from non‐authorizeduser
accounts, have not been evaluated. This may be a useful area for further research to identify
effective methods to minimize the potential harm from piggybacking credit while continuing to
incorporate the predictive information provided by authorized user accounts.
Finally, our analysis has made no attempt to document the extent to which individuals are
engaging in piggybacking credit. Because of the way data is reported to and stored by the credit
bureaus, identifying such conduct is difficult if not impossible. Regardless of how often the practice
is being used, however, we have shown that piggybacking credit has the potential to artificially
improve credit scores at least for specific segments of the population.

References
Avery, Robert B., Raphael W. Bostic, Paul S. Calem, and Glenn B. Canner, 2003, “An Overview of Consumer Data and Credit Reporting,” Federal Reserve Bulletin, vol. 89 (February), pp. 47‐73.
Avery, Robert B., Kenneth P. Brevoort, and Glenn B. Canner, 2009, “Does Credit Scoring Produce a Disparate Impact?” Working paper.
Berney, Louis, 2007, “Crackdown on ‘Piggybacking’ Creates Unintentional Woes,” Cards & Payments, vol. 20(11), pp. 36‐39.
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. Available at
http://www.federalreserve.gov/boarddocs/RptCongress/creditscore/creditscore.pdf (Last visited:
December 11, 2009)
Carson, Keith, and Ezra Becker, 2007, “Authorized User Abuse: Identify and Manage the Risks Associated with Credit Boosting,” TransUnion White Paper. Available at http://www.transunion.com/docs/business/AuthUser_whitepaper_2_09.pdf (Last visited: January 26, 2010).
Harney, Kenneth R., 2007, “Reprieve for the ‘Piggybackers’: Still No Credit‐Score Crackdown,” Washington Post, November 3. Available at http://www.washingtonpost.com/wpdyn/
content/article/2007/11/02/AR2007110201265.html (Last visited: December 11, 2009).
Mays, Elizabeth, 2004, “Scorecard Performance Measures,” Credit Scoring for Risk Managers: The Handbook for Lenders. Ed. Elizabeth Mays. Southwestern: New York, pp. 105‐20.
Pendley, M. Diane, Glenn Costello, and Mary Kelsch, 2007, “The Impact of Poor Underwriting Practices and Fraud in Subprime RMBS Performance,” Fitch Ratings U.S. Residential Mortgage Special Report. November 28.
VantageScore, 2006, Segmentation for Credit Based Delinquency Models White Paper, May. Available at http://www.vantagescore.com/docs/segmentation.pdf (Last visited: December 11, 2009).
VantageScore, 2009, Credit Score Stability in a Changing Risk Environment, July. Available at http://www.vantagescore.com/docs/2009_stability.pdf (Last visited: December 11, 2009).
Yuille, Brigitte, 2007, “Piggybacking Can Lift Your Credit Score,” April 24. Available at http://www.bankrate.com/brm/news/debt/20070424_piggybacking_credit_score_a1.asp (Last visited: December 11, 2009).