Systems and methods for detecting bust out fraud using credit data

• US 8,001,042 B1
• Filed: 10/13/2010
• Issued: 08/16/2011
• Est. Priority Date: 07/23/2008
• Status: Active Grant

• Current Assignee: Experian Information Solutions Incorporated (Experian plc)
• Original Assignee: Experian Information Solutions Incorporated (Experian plc)
• Inventors: Caron, Arielle Renee, Brunzell, Hakan Olof, Sumner, Anthony J., Wong, Tak Wun
• Primary Examiner(s): CAMPEN, KELLY SCAGGS
• Application Number:
  US12/904,088
• Time in Patent Office: 307 Days
• Field of Search: 705/38, 705/35, 705/39, 705/7.28, 705/7.29, 705/7.36, 705/7.38, 705/26
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Systems and methods are for, for example, predicting bust out fraud using credit bureau data. In one embodiment, credit bureau scoring models are created using credit bureau data to detect bust out fraud. The credit bureau scoring models may be then applied to consumer data to determine whether a consumer is involved in bust out fraud.

Claims

1. A method of detecting bust out accounts, wherein bust out accounts exhibit a consumer opening one or more credit accounts across one or more credit issuers and initially exhibiting normal consumer behavior followed by a large number of purchases, cash advances, or other uses of credit before abandonment of the one or more accounts, the method comprising:

accessing, by a computing system, consumer data associated with a consumer;

accessing a bust out scoring model from a storage repository, the bust out scoring model created using credit bureau data and at least one consumer characteristic statistically correlated with predicting bust out accounts; and

periodically applying, using the computing system, the bust out scoring model to the consumer data of the consumer to generate a bust out score for the consumer, wherein the periodically applying the bust out scoring model comprises performing the following method on a periodic basis;

accessing, by the computer system, updated consumer data associated with the consumer;

applying, by the computer system, the bust out scoring model to the updated consumer data to generate the bust out score for the consumer;

determining, by the computer system, whether the updated consumer data exhibits characteristics of bust out accounts by comparing the bust out score to a threshold level; and

in response to the determining that the bust out score associated with the consumer is below the threshold level, providing, by the computing system, an indication to a user indicating that no bust out account has been detected;

in response to the determining that the bust out score associated with the consumer is at or above the threshold level, providing, by the computing system, an indication to the user indicating that a potential bust out account has been detected.

2. A method of detecting bust out accounts, wherein bust out accounts exhibit a consumer opening one or more credit accounts across one or more credit issuers and initially exhibiting normal consumer behavior followed by a large number of purchases, cash advances, or other uses of credit before abandonment of the one or more accounts, the method comprising:

accessing, by a computing system, consumer data associated with a consumer;

accessing a bust out scoring model from a storage repository, the bust out scoring model created using credit bureau data and at least one consumer characteristic statistically correlated with predicting bust out accounts; and

periodically applying, using the computing system, the bust out scoring model to the consumer data of the consumer to generate a bust out score for the consumer, wherein the periodically applying the bust out scoring model comprises performing the following method on a periodic basis;

accessing, by the computer system, updated consumer data associated with the consumer;

applying, by the computer system, the bust out scoring model to the updated consumer data to generate the bust out score for the consumer, wherein the bust out score is configured for comparison to a threshold level in order to determine whether the updated consumer data exhibits characteristics of bust out accounts.

3. The method of detecting bust out accounts of claim 2, further comprising:

receiving the consumer data from a third party, wherein the comparison to the threshold level is performed by the third party.

4. The method of detecting bust out accounts of claim 2, wherein the comparison is performed by the computer system.

5. The method of detecting bust out accounts of claim 2, wherein the periodically applying further comprises:

in response to determining that the bust out score associated with the consumer is below the threshold level, transmitting, by the computing system, an electronic communication to a second computing system indicating that no bust out account has been detected; and

in response to determining that the bust out score associated with the consumer is at or above the threshold level, transmitting, by the computing system, an electronic communication to the second computing system indicating that a potential bust out account has been detected.

6. The method of detecting bust out accounts of claim 2, wherein the consumer data is related to a prospective consumer that may be approved for credit.

7. The method of detecting bust out accounts of claim 2, wherein the bust out scoring model predicts fraud one to three months before abandonment of the bust out account.

8. The method of detecting bust out accounts of claim 2, wherein bust out accounts are characterized by the consumer performing at least one or more actions and demonstrating one or more indications of intent to commit bust out fraud,

wherein the one or more actions comprise an account balance approaching or exceeding the account limit, or payments with bad checks, and

wherein the one or more indications of intent comprise requests for credit limit increase, requests for adding authorized users, frequent balance inquiries, use of balance transfers and convenience checks, or the one or more credit issuers being unable to contact the consumer.

9. The method of detecting bust out accounts of claim 2, wherein the bust out score accounts for a velocity of account utilization changes over a period of time, wherein the velocity of account utilization changes comprises one or more of:

non-payments, delinquency, returned payment, smaller-than-usual payment, or larger-than-usual payments.

10. The method of detecting bust out accounts of claim 2, wherein the one or more credit accounts comprise at least one of a credit card account, a debit card account, or an equity line of credit.

11. A bust out accounts detection system, wherein bust out accounts exhibit a consumer opening one or more credit accounts across one or more credit issuers and initially exhibiting normal consumer behavior followed by a large number of purchases, cash advances, or other uses of credit before abandonment of the one or more accounts, the system comprising:

a computer processor configured to execute modules comprising at least;

a data acquisition module configured to periodically access consumer data associated with a consumer;

a bust out fraud detection module configured to access a bust out scoring model from a storage repository and to periodically apply the bust out scoring model to the consumer data to generate a bust out score for the consumer, the bust out scoring model created using credit bureau data and at least one consumer characteristic statistically correlated with predicting bust out accounts; and

a reporting module configured to perform the following method on a periodic basis;

accessing through the data acquisition module updated consumer data associated with the consumer;

applying through the bust out fraud detection module the bust out scoring model to the updated consumer data to generate the bust out score for the consumer, wherein the bust out score is configured for comparison to a threshold level in order to determine whether the consumer data exhibits characteristics of bust out accounts.

12. The bust out accounts detection system of claim 11, wherein the data acquisition module is further configured to receive the consumer data from a third party, wherein the comparison to the threshold level is performed by the third party.

13. The bust out accounts detection system of claim 11, wherein the comparison is performed by the reporting module.

14. The bust out accounts detection system of claim 11, wherein the reporting module is further configured to:

in response to determining that the bust out score associated with the consumer is below the threshold level, transmitting a first indication to a second computing system indicating that no bust out account has been detected; and

in response to determining that the bust out score associated with the consumer is at or above the threshold level, transmitting a second indication to the second computing system indicating that a potential bust out account has been detected.

15. The bust out account detection system of claim 11, wherein the consumer data is related to a prospective consumer that may be approved for credit.

16. The bust out account detection system of claim 11, wherein bust out accounts are characterized by the consumer performing at least one or more actions and demonstrating one or more indications of intent to commit bust out fraud,

wherein the one or more actions comprise an account balance approaching or exceeding the account limit, or payments with bad checks, and

wherein the one or more indications of intent comprise requests for credit limit increase, requests for adding authorized users, frequent balance inquiries, use of balance transfers and convenience checks, or the one or more credit issuers being unable to contact the consumer.

17. The bust out account detection system of claim 11, wherein the bust out score accounts for a velocity of account utilization changes over a period of time, and wherein the velocity of account utilization changes comprises one or more of:

non-payments, delinquency, returned payment, smaller-than-usual payment, or larger-than-usual payments.

18. The bust out account detection system of claim 11, wherein the one or more credit accounts comprise at least one of a credit card account, a debit card account, or an equity line of credit.