U.S. patent application number 13/417853 was filed with the patent office on 2012-07-05 for system and method for rapid updating of credit information.
This patent application is currently assigned to JPMorgan Chase Bank, N.A.. Invention is credited to Huchen FEI, Xiao HONG, Dong YANG.
Application Number | 20120173406 13/417853 |
Document ID | / |
Family ID | 23140751 |
Filed Date | 2012-07-05 |
United States Patent
Application |
20120173406 |
Kind Code |
A1 |
FEI; Huchen ; et
al. |
July 5, 2012 |
System and Method for Rapid Updating of Credit Information
Abstract
According to one embodiment, the invention relates to a system
and method for evaluating the creditworthiness of an account holder
of a credit account comprising the steps of determining, at least
once a day, whether a first data set relating to the
creditworthiness of the account holder has been received from a
credit reporting organization; determining, at least once a day,
whether a second data set relating to transaction activity of the
credit account has been received; periodically receiving from a
credit reporting organization a third data set relating to the
creditworthiness of the account holder; periodically receiving a
fourth data set relating to the historical activity of the credit
account; and using the first and second data sets, to the extent
they have been received, and the third and fourth data sets to
determine a measure of creditworthiness
Inventors: |
FEI; Huchen; (Newark,
DE) ; YANG; Dong; (Newark, DE) ; HONG;
Xiao; (Hockessin, DE) |
Assignee: |
JPMorgan Chase Bank, N.A.
New York
NY
|
Family ID: |
23140751 |
Appl. No.: |
13/417853 |
Filed: |
March 12, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12636398 |
Dec 11, 2009 |
8160960 |
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13417853 |
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10163301 |
Jun 7, 2002 |
7689506 |
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12636398 |
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60296135 |
Jun 7, 2001 |
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Current U.S.
Class: |
705/38 |
Current CPC
Class: |
G06Q 30/06 20130101;
G06Q 20/14 20130101; G06Q 20/10 20130101; G06Q 20/105 20130101;
G06Q 40/025 20130101; G06Q 30/0283 20130101; G06Q 40/12 20131203;
G06Q 20/102 20130101; G06Q 30/04 20130101; G06Q 40/10 20130101 |
Class at
Publication: |
705/38 |
International
Class: |
G06Q 40/02 20120101
G06Q040/02 |
Claims
1. A computer implemented method, comprising: receiving,
periodically, by at least one computer processor, one or more data
sets comprising data relating to the creditworthiness of an entity
wherein the one or more data sets are received from an
organization; receiving, periodically, by the at least one computer
processor, one or more data sets comprising account transaction
data relating to one or more accounts associated with the entity;
analyzing, by the at least one computer processor, the one or more
data sets comprising data relating to the creditworthiness of the
entity and the one or more data sets comprising account transaction
data, to the extent they have been received, to determine the
presence of a requirement for calculating a measure of
creditworthiness, wherein the requirement is based on at least one
triggering event; and using the one or more data sets comprising
data relating to the creditworthiness of the entity and the one or
more data sets comprising account transaction data, by the at least
one computer processor, to the extent they have been received, to
periodically determine the measure of creditworthiness based on the
requirement using a risk model wherein the periodic determination
is based on the presence of a requirement for calculating the
measure of creditworthiness and the risk model is associated with
an account segment; wherein the at least one computer processor is
located in at least one computing device that is communicatively
coupled to a network.
2. The method of claim 1, wherein periodically receiving the one or
more data sets comprising data relating to the creditworthiness
comprises a pre-determined time interval.
3. The method of claim 1, wherein periodically receiving the one or
more data sets comprising account transaction data comprises a
pre-determined time interval.
4. The method of claim 1, further comprising: defining, by the at
least one computer processor, a plurality of account holder
segments based on at least one characteristic of the account
holders; defining, by the at least one computer processor, at least
one risk model for each of the plurality of account holder
segments.
5. The method of claim 1, wherein the triggering event comprises
one of: a receipt of payment, a transaction greater than a
predetermined amount, or any significant event related to an
account associated with the entity.
6. The method of claim 1, wherein the one or more data sets
comprising data relating to the creditworthiness of the entity
comprise data derived from multiple creditors and accounts.
7. The method of claim 6, wherein the multiple accounts comprise
one or more of: a mortgage account, an auto loan account, a school
loan account, and a credit card account.
8. The method of claim 1, wherein the organization is a credit
reporting organization.
9. The method of claim 1, wherein the one or more data sets
comprising data relating to the creditworthiness of the entity
comprise data related to one or more credit accounts.
10. The method of claim 9, wherein the one or more credit accounts
are associated with the entity.
11. The method of claim 7, wherein the multiple accounts are
associated with the entity.
12. The method of claim 11, wherein the entity comprises a
customer.
13. The method of claim 1, wherein the entity comprises a
customer.
14. The method of claim 1, wherein the one or more data sets
comprising data relating to the creditworthiness are received from
a credit reporting organization.
15. The method of claim 14, wherein the one or more data sets
comprising data relating to the creditworthiness further comprise a
credit history data set.
16. The method of claim 15, further comprising: analyzing the
credit history data set; generating a set of dependent variables
using weight functions or ratios; creating a credit history profile
from the credit history data set using the set of dependent
variables, wherein the credit history profile comprises a summary
of the data in the credit history data set; and transmitting the
credit history data set as an input to periodically determine the
measure of creditworthiness.
Description
[0001] The present application is a continuation of and claims
priority to U.S. patent application Ser. No. 12/636,398 filed on
Dec. 11, 2009, which is a continuation of and claims priority to
U.S. patent application Ser. No. 10/163,301, filed on Jun. 7, 2002,
entitled "System and Method for Rapid Updating of Credit
Information," which claims priority to U.S. Provisional Application
No. 60/296,135, filed Jun. 7, 2001. The disclosure of these
priority applications are incorporated herein by reference in their
entirety to the extent that it is consistent with this invention
and application
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The invention relates to the field of electronic
transactions, and more particularly to techniques for rapidly
updating credit scores or other credit information, for instance on
a daily or greater basis.
[0004] 2. Description of the Related Art
[0005] The credit card, mortgage, personal credit and other
financial sectors rely on a variety of information in reviewing,
approving, denying and otherwise evaluating credit and credit
risk.
[0006] One commercially known metric for assessing credit risk is
the mathematical model generated by Fair, Issac and Company (FICO)
which assigns consumers a normative score based on credit files and
other information. Credit files themselves, such as those
maintained by the credit reporting organizations (such as Equifax
and Experian), may receive updated account payment, balance,
delinquency and other information on a periodic basis, which is
typically monthly. The First Data Resources Corporation (FDR)
likewise commercially handles score calculation generally on a
monthly basis. One known FDR risk score is based on historical data
of a particular credit card account, including daily transaction
data for the account. However, the FDR risk score is not based on
credit reporting organization data.
[0007] Other methods and systems are known which generate credit
scores on a monthly basis using bimonthly data from credit
reporting organizations and monthly historical data for a
particular account.
[0008] Financial institutions such as credit card issuers use the
credit scores and data to determine whether and to what extent to
extend credit to a consumer. Credit card issuers may rely on
automated scoring engines which use the credit scores and data to
determine to what extent to extend credit to an existing
cardholder. In a certain percentage of cases, the credit card
issuer, based on the scoring engine, will extend credit to a
consumer who then fails to repay the loan. The profit of a credit
card issuer is thus affected by the predictive capability of the
scoring engine. A scoring engine which reduces the instances of
default by even a small percentage can have a significant effect on
the profit of the credit card issuer.
BRIEF SUMMARY OF THE INVENTION
[0009] According to one embodiment, the invention relates to a
system and method for evaluating the creditworthiness of an account
holder of a credit account comprising the steps of determining, at
least once a day, whether a first data set relating to the
creditworthiness of the account holder has been received from a
credit reporting organization; determining, at least once a day,
whether a second data set relating to transaction activity of the
credit account has been received; periodically receiving from a
credit reporting organization a third data set relating to the
creditworthiness of the account holder; periodically receiving a
fourth data set relating to the historical activity of the credit
account; and using the first and second data sets, to the extent
they have been received, and the third and fourth data sets to
determine a measure of creditworthiness.
[0010] According to another embodiment, the invention relates to a
system and method for determining the creditworthiness of an
account holder comprising the steps of receiving a credit history
data set from a credit reporting organization on a periodic basis;
receiving an account history data set on a periodic basis;
determining, at least once a day, whether a third data set relating
to the creditworthiness of the account holder has been received;
and using the credit history data set, the account history data
set, and the third data set to determine a measure of the
creditworthiness of the account holder.
[0011] The invention can provide significant advantages in
predicting credit risk, due in part to: (a) the utilization of data
from a credit reporting organization in addition to data from the
account holder's historical behavior in a particular account, and
(b) the utilization of long-term reports, e.g., a bimonthly credit
reporting organization report, and a monthly account history data
set, in addition to daily reports, e.g., a daily credit reporting
organization report for significant events and a daily account
transaction data set for the latest transaction activity. This
information allows the risk model to reflect both historical
behavior and very recent behavior in the account holder's entire
recorded credit behavior, rather than his or her behavior in one
account. The risk model can therefore provide a significant
improvement in the accuracy of predicting defaults.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 illustrates a method for evaluating creditworthiness
according to one embodiment of the invention;
[0013] FIG. 2 illustrates an example of a system which can be used
to carry out a method according to an exemplary embodiment of the
invention;
[0014] FIG. 3 illustrates a scoring engine which may be used in
practicing the method shown in FIG. 1 according to an exemplary
embodiment of the invention;
[0015] FIG. 4 shows an example of a number of account holder
segments which can be used in connection with the scoring engine of
FIG. 3 according to an exemplary embodiment of the invention;
[0016] FIG. 5 illustrates a method for evaluating creditworthiness
according to another embodiment of the invention; and
[0017] FIG. 6 shows an example of the timing of data processing
according to an exemplary embodiment of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0018] The present invention relates to a method and system for
evaluating creditworthiness. According to one embodiment, the
method and system produce, among other things, a score ranging from
0 to 980 which can be used to decide whether and to what degree to
extend credit to a consumer. For example, the score may be used to
decide whether to authorize a particular credit transaction,
whether to approve or change an account holder's credit limit, or
what terms to offer in reissuing an account. The score is derived
from data relating to the creditworthiness of a particular account
holder, which data may be maintained by one or more entities.
[0019] FIG. 1 is a diagram which illustrates a system and method
for evaluating creditworthiness according to one embodiment of the
invention. As shown in FIG. 1, the system and method include a
scoring engine 150 which outputs a score indicative of
creditworthiness. The scoring engine 150 receives data from a
credit data reporting routine 100 and an account data processing
routine 120. The credit data reporting routine 100 is typically
carried out by one or more credit reporting organizations (also
sometimes referred to as credit bureaus) such as Experian,
TransUnion, and Equifax. The credit reporting organizations have
agreements with various creditors under which the creditors provide
credit data 102 to the credit reporting organizations relating to
the behavior of the creditor's account holders. Such agreements
enable the credit reporting organizations to compile credit reports
in various forms which include detailed information about the
credit behavior of account holders. The credit report for a
particular person or entity typically includes data from many
credit accounts, e.g., mortgage, auto loan, credit card, school
loan, etc.
[0020] The credit reporting organizations offer credit reports in a
standard format to consumers for a fee. The credit reporting
organizations may also provide credit reports in a customized
format for creditors such as banks issuing credit cards. For
example, as shown in FIG. 1, the credit report generation involves
the generation of two customized credit reports, a credit history
data set 104 and a significant events data set 106.
[0021] As shown in FIG. 1, a second portion of the system and
method for evaluating creditworthiness involves an account data
processing routine 120. The account data processing routine 120
involves, among other things, receiving account transaction data
112 (e.g., data relating to credit card purchases) from merchants
and manipulating or processing the account transaction data 112
into a format which is useful for predicting creditworthiness. For
example, as will be described below, the account data processing
routine 120 may involve receiving credit card transaction data and
generating an account history data set 122 (also sometimes referred
to as a "master file"), which may be generated monthly, and an
account transactions data set 124, which may be generated daily.
The account history data set 122 and the account transactions data
set 124 are used as input to the scoring engine 150.
[0022] The account data processing routine 120 typically receives
account transaction data 112 relevant to a single account, such as
a credit card account provided by a bank issuing credit cards. The
account transaction data 112 may be supplied, for example, by an
entity which processes credit card transactions, commonly referred
to as "the acquiring processor."
[0023] The account data processing routine 120 may be executed on a
computer system maintained by the account provider, e.g., the
credit card issuing bank. The credit card issuing bank receives the
account transaction data 112 and creates and maintains the account
history data set 122 and the account transactions data set 124.
However, if desired, these functions may also be handled by a
separate account data processing entity. The account data
processing entity may be, for example, an entity such as First Data
Resources Corporation (FDR) which provides credit and debit card
processing services to financial institutions such as banks which
issue credit and debit cards.
[0024] A third portion of the system and method shown in FIG. 1 for
evaluating creditworthiness involves a scoring engine 150 which, as
will be described below, includes at least one risk model. The risk
model is a routine which typically receives as input a credit
history data set 104, a significant events data set 106, an account
history data set 122, and an account transactions data set 124, and
which outputs a score indicative of creditworthiness from 0 to 980,
with 980 being the highest credit risk. However, the risk score can
be based on any desired combination of input data sets 104, 106,
122, 124.
[0025] The processes depicted in FIG. 1 can be carried out on a
system as shown in FIG. 2 which includes computers or computing
devices 50, such as server computers, connected via a communication
links 60 to a network 70 such as the internet. The computers 50 are
programmed to exchange information via the network 70. The
computers 50 each typically include a database for storing
information. Each computer 50 may be adapted to send and receive
information to multiple users over a network, as is well known in
the art.
[0026] The data files which provide input to the scoring engine 150
will now be described with reference to FIG. 1. Typically, the
credit history data set 104 and the significant events data set 106
are generated by one or more credit reporting organizations. The
account history data set 122 and the account transactions data set
124 may be generated by the financial institution which issues the
account, e.g., a credit card issuer, or may be generated by another
entity which processes the account information.
[0027] The credit history data set 104 typically comprises a
borrower-specific file which includes data on the borrower's
historical credit behavior. The data are typically derived from
multiple creditors and accounts, e.g., mortgage, auto loan, school
loan, credit card, etc. Examples of variables which may be included
in the credit history data set 104 include: current balance,
repayment schedule, lateness history, delinquency, age of account,
number of various accounts, open date of various accounts, lateness
information, if any, of various accounts, credit limit of various
accounts, loan amount of various accounts, etc. The credit history
data set 104 is typically transmitted periodically, e.g., every two
months, by the credit reporting organization to the entity running
the scoring engine 150, e.g., a bank issuing credit cards.
[0028] The significant events data set 106 contains data derived
from multiple creditors and accounts relating to events which are
significant to a person's creditworthiness. For example, the
significant events data set 106 may include recent changes in
account balance over a certain dollar amount, bankruptcy filing, a
delinquency greater than an arbitrary time period, receipt of an
arbitrary payment amount, credit inquiries, new account openings,
etc.
[0029] The significant events data set 106 is typically maintained
by a credit reporting organization and may be sent to the entity
running the scoring engine 150 on a daily basis in the event that
there is a new significant event to report. In the case of no new
significant event, the significant event data set 106 is either not
delivered or contains a null value representing the absence of a
new significant event.
[0030] The other two data sets, i.e., the account history data set
122 and the account transactions data set 124 contain data relating
to a particular credit account of the account holder. The account
history data set 122 may contain a number of variables related to
the account activity and characteristics for a particular holder of
an account. The account history data set 122 may contain a
relatively large amount of data, because the creditor is the entity
which provides the account to the account holder and thus has
typically maintained detailed records of the account holder and
account activity over an extended time period.
[0031] Examples of variables which may be included in the account
history data set 122 include total transaction dollars, number of
transactions, payment amount, lateness, merchant balance, cash
balance, balance transfer amount, cardmember service (CMS) calling
information (e.g., information on calls made by cardmembers to CMS
such as number of calls, time of calls, subject matter of calls),
etc. The account history data set 122 may be generated on a monthly
basis, for example, and may contain data relevant to the previous
12 months of account activity.
[0032] The account transactions data set 124 typically contains
data on recent credit card transactions. It contains such
information as amount of transaction, merchant, date and time of
transaction, location of merchant, type of merchant, available
credit at the time, etc. According to one embodiment, the account
transactions data set 124 is generated on a daily basis and used as
input to the scoring engine 150.
[0033] The scoring engine 150, as shown in FIG. 3, receives the
input data sets 104, 106, 122 and 124 and outputs a score
indicative of the creditworthiness of the account holders. As a
preliminary step, a trigger routine 152, as shown in FIG. 3, may be
executed to determine whether a particular account needs to be
scored. The process of scoring an account has a cost associated
with it. For example, if the scoring process is performed by an
entity retained for that purpose, the entity will typically charge
a fee based on the number of accounts scored. Whether the scoring
is performed in-house or by a third party, the computer resources
and file transfer process will have an associated cost, which may
be avoided by the triggering routine.
[0034] According to an exemplary embodiment of the invention, the
triggering routine 152 is executed initially to determine whether
the account data has changed in such a manner or extent as to
justify the cost of scoring a particular account. The triggering
routine involves examining one or more variables, typically
existing in the significant events data set 106 or the account
transactions data set 124. For example, the triggering routine 152
may check these data sets to ascertain whether a payment has been
received, a payment reversal has taken place (e.g., a bounced
check), an authorization has been granted over a certain dollar
amount, a balance change greater than a certain amount has taken
place, or one of the events in the significant events file 106 has
occurred. The triggering routine may also examine a "cycle"
variable in the account history data set 122 which forces the
calculation of a score at least once every specified cycle in the
event that no other triggers have caused a score to be
calculated.
[0035] To enhance the predictive capability of the scoring engine
150, a number of different risk models may be constructed which
correspond to different segments of the account holder population.
The segments are defined by the behavior of the account holders.
For example, as shown in FIG. 4, the account holder population may
be segmented based on the age of the account (i.e., the "Months on
Book" or MOB), whether the account is current, delinquent 30 days,
or delinquent 60 days, the utilization of the credit line ("Util",
which refers to the balance of the account at a specified time
divided by the credit limit), and whether the account has a zero
balance and is inactive. For relatively new accounts (e.g., Months
on Book<3 months), as shown in FIG. 4, a first segment may be
defined for first day accounts ("First Day"), and a second segment
may be defined for accounts having an age of 2 days through the end
of the second month ("Early Month").
[0036] After the account holder is classified into a particular
segment, the risk model for that segment is utilized to generate a
risk score, for example on a scale of 0 to 980. A number of risk
models are constructed in order to enhance the predictive
capability of the scoring engine. Each risk model is designed to
predict risk with respect to a particular segment of the account
holder population.
[0037] For example, segment 1 may be defined for first day
accounts. If credit reporting organization data is not available
for a particular account holder, then the score may be based on
total first day transactions amount and open-to-buy amount (i.e.,
credit limit minus total transactions amount). If credit reporting
organization data is available, then the number of active accounts
(also sometimes referred to as active "trades"), amount of retail
accounts, and total revolving accounts balance may also be used for
scoring.
[0038] The risk models typically take the following form:
Score=exp(a.sub.1x.sub.1+ . . .
+a.sub.nx.sub.n)/[1+exp(a.sub.1x.sub.1+ . . . +a.sub.nx.sub.n)]
where the variables x.sub.1, x.sub.2, . . . , x.sub.n are the
parameters discussed above (e.g., total revolving accounts balance,
amount of retail accounts, etc.), and the coefficients a.sub.1,
a.sub.2, . . . , a.sub.n are chosen according to desired criteria
of the account provider.
[0039] Referring again to FIG. 3, each segment may have two risk
models associated with it. For example, in FIG. 3, segment 1 has
risk models RM 1A and RM 1B, segment 2 has risk models RM 2A and RM
2B, and so on. The first risk model, e.g., RM 1A is constructed to
receive as input the credit history data set 104, the significant
events data set 106, the account history data set 122, and the
account transactions data set 124. The second risk model, e.g., RM
1B, may be constructed for those account holders who have no credit
history data set 104 or significant events data set 106, for
example because the credit reporting organization has no records of
their credit history.
[0040] Once the correct segment is ascertained, data from the input
data sets is used as input to the scoring engine 150 to calculate a
risk score. The risk score is typically based on a significant
amount of historical data from the credit history data set 104 and
account history data set 122. The risk score is also typically
based on very recent data from the significant events data set 106
and the account transactions data set 124. Due to the combination
of a significant amount of historical data and very recent data,
the risk model is thus able to provide improved accuracy in
predicting credit risk. The inclusion of recent data, for example,
allows the issuing bank to deny credit to any account exhibiting
recent activity indicative of increased credit risk.
[0041] FIG. 5 is a diagram which illustrates a system and method
for evaluating creditworthiness according another embodiment of the
invention. As shown in FIG. 5, the system and method include a
scoring engine 250 which outputs a score indicative of
creditworthiness. The scoring engine 250, which may be part of an
account data processing routine 270, receives input data 262 from a
credit data reporting routine 260 and receives input data 238, 282
generated in the account data processing routine 270.
[0042] The credit data reporting routine 260 provides data 262
relating to the credit history and creditworthiness of the account
holder. The credit data reporting routine 260 may be executed by a
credit reporting organization, for example, which provides a
customized data set to the issuing bank. The account data
processing routine 270 processes historical data for the particular
accounts of the account provider (e.g., the bank issuing credit
cards). The account data processing routine 270 may be executed by
a credit card issuing bank, for example, or it may be executed by a
separate processing entity which the credit card issuing bank
retains to process the account transactions of the bank's account
holders.
[0043] The scoring engine 250 includes at least one risk model, and
typically includes a number of risk models corresponding to a
number of segments of the account holder population, as described
above with reference to FIG. 4. The functions and processes
depicted in FIG. 5 can be carried out on the system shown in FIG.
2. The process of generating the files 262, 238, 282 which are
input to the scoring engine 250 will now be described with
reference to FIG. 5.
[0044] The credit reporting organization receives credit data 202
relevant to particular borrowers from a number of creditors which
have agreements with the credit reporting organization to provide
such data. The credit reporting organization uses the credit data
202 to create a credit history data set 264 on a periodic basis,
for example monthly. The credit history data set 264 typically
contains the same variables described above with respect to the
credit history data set 104.
[0045] The next step in the credit data reporting routine 260
involves the creation of a credit history profile 266 from the
credit history data set 264. The credit history profile 266
summarizes the data in the credit history data set 264 using a set
of derived variables called profilers. The profilers are generated
from the credit history data set 264 using weight functions or
ratios. Each profiler routine comprises an algorithm which takes as
input certain variables of the credit history data set 264 and
which outputs a real number. For example, "debt burden ratio" is an
example of a profiler, defined as the total revolving balance
divided by the highest bankcard credit line limit. Another example
of a profiler is the "average revolving balance velocity," which
may be defined as total credit balance divided by the average age
of bankcard accounts. The profilers can be constructed according
any desired criteria of the account provider.
[0046] In general, the profilers summarize the data in the credit
history data set 264 and therefore can be updated with current data
using less computer resources than would be required to update the
credit history data set 264, which may be a relatively large file.
Consequently, the profilers facilitate the transmission of updated
credit history data to the scoring engine 250 at a relatively high
frequency, e.g., daily.
[0047] In the next step of the credit data reporting routine 260,
the credit reporting organization reports significant events in a
significant events data set 268. The significant events data set
268 contains data derived from multiple creditors and accounts
relating to events which are significant to a person's
creditworthiness. For example, the significant events data set 268
may include changes in account balance over a certain dollar
amount, bankruptcy filing, a delinquency greater than an arbitrary
time period, receipt of an arbitrary payment amount, lateness,
inquiries, etc. The significant events data set 260 typically
contains the same variables described above with respect to data
set 106.
[0048] The significant events data set 268 is used to update the
credit history profile 266. The credit history profile 266 is
updated by applying a weight function to the significant events
data set 268 and to the previous credit history profile 266. The
updated credit history profile 262 thus is based in part on recent,
e.g., daily, data relating to significant events in the credit
history of the account holders. The updated credit history profile
262 is used as input to the scoring engine 250.
[0049] Referring now to the account data processing routine 270,
that routine involves, among other things, receiving account
transaction data 222 (e.g., data relating to credit card purchases)
from merchants, typically via an acquiring processor, and
manipulating or processing the account transaction data 222 into a
format which is useful for predicting creditworthiness. The account
data processing routine 270 generates an account history profile
282 on a periodic basis, e.g., monthly, and an updated account
transactions profile 238 on a periodic basis, e.g., daily, which
are input to the scoring engine 250.
[0050] The account transaction data 222 received by the account
data processing routine 270 typically comprises data on
transactions of only the accounts provided by the account provider
running the scoring engine 250. For example, the account
transaction data 222 may comprise data on transactions executed by
the card holders of an issuing bank's credit cards. The account
transaction data 222 may be supplied, for example, by one or more
entities which processes credit card transactions, such as one or
more acquiring processors. The entity which runs the account data
processing routine 270 uses the account transaction data 222 to
create an account transactions data set 232 which contains
relatively recent data on account transactions. The account
transactions data set 232 typically contains the same variables
described above with respect to account transactions data set
124.
[0051] Another step in the account data processing routine 270
involves the creation of an account transactions profile 234 from
the account transactions data set 232. The account transactions
profile 234 summarizes the data in the account transactions data
set 232 using a set of derived variables called profilers, as
described above. The profilers may be generated from the account
transactions data set 232 using weight functions. Each profiler
routine comprises an algorithm which takes as input certain
variables of the account transactions data set 232 and which
outputs a real number. The profilers summarize the data in the
account transactions data set 232 and therefore can be updated with
current data using less computer resources than would be required
to update the account transactions data set 232, which may be
typically a relatively large file. Consequently, the profilers
facilitate the transmission of updated account transactions data to
the scoring engine 250 at a relatively high frequency, e.g., daily.
As described above, the profilers for the account data processing
routine 270 can be constructed according any desired criteria of
the account provider.
[0052] In another step of the account data processing routine 270,
a recent account transactions data set 236 is received from one or
more acquiring processors and used to update the account
transactions profile 234. The recent account transactions data set
236 contains data relevant to recently executed credit card
transactions, such as amount of transaction, date and time of
transaction, merchant, location of merchant, type of merchant,
available credit at the time, etc.
[0053] The account transactions profile 234 is updated by applying
a weight function to the recent account transactions data 236 and
the previous account transactions profile 234. The updated account
transactions profile 238 thus is based in part on recent, e.g.,
daily, data relating to account transactions. The updated account
transactions profile 238 is used as input to the scoring engine
250.
[0054] FIG. 5 also shows that the account data processing routine
270 periodically (e.g., monthly) generates an account history data
set 280 (also known as a "master file") which contains a relatively
large number of variables relating to historical activity of the
account. The account history data set 280 typically contains the
same variables described above with respect to account history data
set 122.
[0055] The account history data set 280 typically comprises data
spanning 12 months, and may be structured as 12 monthly data sets,
for example. The account history data set 280 is used to create an
account history profile 282 using a number of profiler routines, as
described above in connection with the account transactions profile
234. The account history profile 282 is used as input to the
scoring engine 250.
[0056] If the account data processing routine 270 is being executed
at the end of the month, at which time a new entire month of
account history data is available, the account history profile 282
is updated with the newly available account history data, as shown
in the upper portion of FIG. 5.
[0057] The input data to the scoring engine 250 includes the
account history profile 282, the account transaction profile 238,
and the updated credit history profile 262. As a preliminary step,
a trigger routine may be executed to determine whether a particular
account needs to be scored, as described above with respect to FIG.
3. For the accounts which trigger scoring, the profilers are the
input to the scoring engine 250. As described above, the scoring
engine 250 typically includes a number of account holder segments,
such as those shown in FIG. 4. The various segments include risk
models which are customized for the particular characteristics of
the segment population in order to enhance the predictive
capabilities of the scoring engine 250. Each segment may have two
risk models, a first risk model which receives the account
transactions profile 238, the account history profile 282, and the
credit history profile 262, and a second risk model which receives
only the account transaction profile 238 and the account history
profile 282, e.g., because the credit reporting organization has no
data on the account holder.
[0058] As described above, the risk model is typically of the
form:
Score=exp(a.sub.1x.sub.1+ . . .
+a.sub.nx.sub.n)/[1+exp(a.sub.1x.sub.1+ . . . +a.sub.nx.sub.n)]
where the variables x.sub.1, x.sub.2, . . . , x.sub.n are the
profilers discussed above and the coefficients a.sub.1, a.sub.2, .
. . , a.sub.n are chosen according to desired criteria of the
account provider.
[0059] After the scoring engine 250 has scored the accounts, the
account history data set 280 is updated, as shown on the right side
of FIG. 5. This update typically involves updating only the latest
month of account history data in the account history data set
280.
[0060] According to another aspect of the invention, a feature
known as a "mimic routine" or "mimic algorithm" may be applied to
data in the account history data set 280 according to an exemplary
embodiment of the invention. The account history data set 280
enhances the predictive capability of the scoring engine 250
because, among other things, it typically includes data spanning a
12-month period. However, because it is typically a relatively
large file, the data processing resources required to process the
account history data set 280 on a daily basis can be large.
[0061] Accordingly, the inventors have developed mimic routines
which produce a single value representative of a plurality of
historical values for a particular variable in the account history
data set 280. The mimic routines typically have the form of a
weighted average:
M=1/n(a.sub.1x.sub.1+a.sub.2x.sub.2+a.sub.3x.sub.3+ . . .
+a.sub.nx.sub.n)
where the coefficients a.sub.n represent the weighting factors and
the x.sub.n represent the file variables from the account history
data set 280. Typically, the most recent month is weighted more
heavily than the oldest month. The mimic routines are used in
connection with the process of creating and updating the account
history profile 282 with the profiler routines. For example, a
mimic routine may comprise a portion of a profiler routine.
[0062] The mimic routines generally have two forms. The first form
converts a plurality of historical values of a particular variable
from the account history data set 280 into a single value
representative of the entire time span. For example, a mimic
routine may take as input 12 monthly values of a account history
data set variable and output a single value representative of the
12 monthly values. This process may be repeated for any desired
variables in the account history data set 280. The first form is
applied initially to data in the account history data set 280 to
derive a single value for desired variables having multiple
historical values, e.g., 12 monthly values.
[0063] One example of the first form of mimic routine relates to a
weighted average of the monthly balance amounts. For example, the
account history data set 280 may include the monthly balance values
Bal(1), Bal(2), . . . , Bal(12). A mimic routine may be defined to
calculate an "avgbal(12)" variable as follows:
avgbal(12)=a.sub.1*Bal(1)+a.sub.2*Bal(2)+ . . .
+a.sub.12*Bal(12)
where the coefficients a.sub.1, a.sub.2, . . . , a.sub.12 are
selected according to any desired criteria, e.g., to maximize the
predictive power of avgbal(12).
[0064] The second form of mimic routine is used to update a
previous output value from a mimic routine based on a new value in
a new account history data set 280. In particular, the second form
of mimic routine receives two inputs, (1) the output from a
previously executed mimic routine, and (2) a new account history
data set 280 variable. The second form of mimic routine may also
utilize a weighted average of the two values, or other desired
equation, to update the output of the mimic routine. According to
one example, a mimic routine "avgbal" for month n+1 (the new month)
is defined as:
avgbal(n+1)=a*avgbal(n)+(1-a)*bal(n+1)
where a is the desired weighting factor.
[0065] The second form of mimic routine provides the advantage that
the previous output of any mimic routine can be easily updated.
Thus, initially, the first form of mimic routine may be applied to
a number of historical values of a variable in the account history
data set 280 to output a single value representative of all the
historical values. Next, when a new account history data set 280 is
received, e.g., in one month, the second form of mimic routine is
used to update the output with the new value from the new account
history data set 280.
[0066] Besides the two aforementioned general forms of mimic
routines, mimic routines may also be used to mimic moving
summations and moving maxima/minima, e.g., sum of cash advance in
past 12 months, the total number of late fees charged in the past
24 months, the maximum balance in the last 12 months, etc.). These
moving sum and moving maxima/minima typically can have significant
power in predicting credit risk. The mimic algorithms simulate the
moving sum and moving maxima/minima without the need to store all
the time series data. The moving summation and moving maxima/minima
type of mimic algorithms may be used, for example, as a part of a
profiler in the process of creating the account history profile
282.
[0067] An example of a summation-type mimic algorithm will now be
described. The objective of this exemplary mimic algorithm is to
determine SX(t), which is an estimate of (i.e., mimics) SUMX(t).
SUMX(t) is defined as:
SUMX(t)=sum(X(t)+X(t-1)+ . . . +X(t-n))
where X(t) is the balance at month t. The objective is to calculate
SX(t+1) without carrying the variables for the previous n months,
as would be required to calculate SUMX. The algorithm involves
defining Y(n,t)=X(t-n), which is an estimate of the balance X for
the oldest month t-n. SX(t+1) can be determined as follows:
SX(t+1)=SX(t)+X(t+1)-Y(t)
Y(t) is determined from the following equation:
Y(t+1)=a*Y(t)+(1-a)*X(t+1)
wherein the factor a=1-1/n. The mimic algorithm thus allows SX,
which mimics SUMX, to be calculated without carrying the variables
for the previous n months. The value for SX can then be used as
part of a profiler, for example to create the account history
profile 282.
[0068] The risk score output by the risk model is used as a basis
for making decisions relating to the extension of credit, such as
whether to change an account holder's credit line, whether to
authorize a particular credit card transaction, and how to define
the terms of new credit accounts in marketing them to prospective
account holders. The invention provides significant advantages in
predicting credit risk, due in part to: (a) the utilization of data
from a credit reporting organization in addition to data from the
account holder's historical behavior in a particular account, and
(b) the utilization of long-term reports, e.g., a bimonthly credit
reporting organization report, and a monthly account history data
set, in addition to daily reports, e.g., a daily credit reporting
organization report for significant events and a daily account
transaction data set for the latest transaction activity. This
information allows the risk model to reflect both historical
behavior and very recent behavior in the account holder's entire
recorded credit behavior, rather than his or her behavior in one
account. The risk model can therefore provide a significant
improvement in the accuracy of predicting defaults.
[0069] FIG. 6 illustrates an example of the timing of a daily
calculation according to an exemplary embodiment of the invention.
As shown in FIG. 6, Authorization ("Auth") data from authorized
credit card transactions and posted monetary ("PM") data are
received at 6 pm by a routine which updates the account history
data set (also sometimes referred to as the cardholder master file
(CMF)). At 11 am, the credit history data set (e.g., "bureau
bi-month") is received, and at 3 pm the significant events data set
(e.g., "daily trigger") is received. All the data is held until 2
am, at which time triggers are evaluated to determine which
accounts should be scored. The accounts which have been triggered
for scoring are scored from 2 am to 6 am. Thus, by 6 am, an up to
date score is obtained for evaluation of whether and to what extent
to extend credit to each account.
[0070] While the foregoing description includes details and
specific examples, it is to be understood that these have been
included for purposes of illustration only, and are not to be
interpreted as limitations of the present invention. Modifications
to the embodiments described above can be made without departing
from the spirit and scope of the invention, which is intended to be
encompassed by the following claims and their legal
equivalents.
* * * * *