TRANSACTION AUTHORIZATION USING TIME-DEPENDENT TRANSACTION PATTERNS

• US 20100280950A1
• Filed: 05/04/2010
• Published: 11/04/2010
• Est. Priority Date: 05/04/2009
• Status: Abandoned Application

• Current Assignee: Kevin P. Siegel, Patrick Faith
• Original Assignee: Kevin P. Siegel, Patrick Faith
• Inventors: Faith, Patrick, Siegel, Kevin P.
• Application Number:
  US12/773,767
• Publication Number:
  US 20100280950A1
• Time in Patent Office: Days
• US Class Current: 705/44
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 20/40 Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Systems, apparatus, and methods for authorizing a transaction initiated by a consumer are provided. A likelihood function can approximate a pattern of previous transactions and provide a measure of how likely it is for a transaction to occur as a function of time. The time of a current transaction can be used to determine a corresponding likelihood value of a likelihood function associated with the transaction. The likelihood value can then be used to determine a score for authorizing the transaction. As the likelihood corresponds to a particular time of a pattern, the score can be tailored to the current transaction and achieve greater accuracy.

Claims

1. A method of determining authorization of a transaction involving a consumer, the method comprising:

receiving data associated with the transaction, wherein the data includes a time T of the transaction;

identifying a first likelihood function associated with the consumer and with the transaction, wherein the first likelihood function approximates one or more patterns of previously performed transactions, the first likelihood function having a respective likelihood value for each of a plurality of times;

a computer system determining a first likelihood value of the first likelihood function, the first likelihood value providing a measure of a likelihood of the transaction occurring at the time T; and

determining a score using the first likelihood value, the score adapted to be used to determine authorization of the transaction.

2. The method of claim 1, further comprising:

receiving data corresponding to previous transactions;

associating one or more keys with each previous transaction;

correlating pairs of previous transactions, each correlated pair associated with a particular pair of keys; and

for each correlated pair, determining time intervals between the transactions of the correlated pair; and

for each key pair;

tracking numbers of occurrences of correlated pairs having time intervals within specified time ranges, the transactions of the correlated pairs being associated with corresponding keys of the key pair,

wherein a likelihood function for a pattern of a key pair includes the numbers of occurrences of correlated pairs for a key pair.

3. The method of claim 2, wherein the previous transactions are associated with the consumer and/or with one or more other consumers similar to the consumer.

4. The method of claim 1, wherein identifying the first likelihood function associated with the transaction includes:

associating one or more keys with the transaction, wherein a likelihood function is associated with an initial key and a final key; and

identifying the first likelihood function as being associated with a first final key that matches one of the keys associated with the transaction.

5. The method of claim 4, further comprising:

matching the first final key with one of the keys associated with the transaction includes broadening the one of the keys associated with the transaction until the one of the keys associated with the transaction is equal to a final key of one of a plurality of likelihood functions.

6. The method of claim 4, further comprising:

identifying other likelihood functions associated with the consumer and with the transaction; and

the computer system determining another likelihood value for each of the likelihood functions,

wherein determining the score uses the another likelihood values.

7. The method of claim 1, wherein the first likelihood function approximates one or more patterns of fraudulent transactions, and wherein the score includes a measure of a likelihood that the transaction is fraudulent.

8. The method of claim 1, wherein the first likelihood function approximates one or more patterns of transactions previously performed by the consumer, and wherein the score includes a measure of a likelihood that the transaction is consistent with the one or more patterns.

9. The method of claim 1, wherein the first likelihood function approximates one or more patterns of transactions previously performed by consumers to which the consumer is considered similar, and wherein the score includes a measure of a likelihood that the transaction is consistent with the one or more patterns.

10. The method of claim 1, further comprising:

transmitting the score to another entity involved in the transaction.

11. The method of claim 10, wherein the another entity is an issuer of a credit account being used for the transaction.

12. The method of claim 1, wherein the data associated with the transaction is received from a merchant involved in the transaction.

13. The method of claim 12, wherein the data associated with the transaction is received from the merchant in a request for authorization of the transaction, the method further comprising:

providing an approval to the authorization request when the transaction is authorized based on the score.

14. The method of claim 1, wherein determining the score using the likelihood value includes inputting the first likelihood value into a neural network that has been trained with valid and fraudulent transactions.

15. The method of claim 1, wherein the transaction data includes a first amount of the transaction, wherein the first likelihood function is associated with a range of amounts that includes the first amount, and wherein other likelihood functions are associated with different ranges of amounts.

16. The method of claim 1, wherein the computer system is part of a payment processing network.

17. A computer program product comprising a tangible computer readable medium storing a plurality of instructions for controlling one or more processors to perform the method of claim 1.

18. A computer system comprising:

one or more processors; and

the computer program product of claim 17.

19. A method of determining authorization of a transaction involving a consumer, the method comprising:

receiving data associated with the transaction, wherein the data includes a time T of the transaction;

identifying one or more tables associated with the consumer and with the transaction, each table including a plurality of values organized along at least one axis, wherein a first axis of each table corresponds to a plurality of different time ranges, and wherein the values in each table correspond to a number of previous transactions occurring within one of the different time ranges;

a computer system determining at least one value from each table, wherein the at least one value is for a time range in which the time T corresponds; and

determining a score using the at least one value, the score adapted to be used to determine authorization of the transaction.

20. The method to claim 19, wherein each table includes a second axis corresponding to different ranges of amounts for previous transactions.

21. The method of claim 19, wherein identifying the one or more tables associated with the transaction includes:

associating one or more keys with the transaction, wherein a table is associated with an initial key and a final key; and

identifying the at least one table as being associated with a first final key that matches one of the keys associated with the transaction.