FREQUENCY-BASED TRANSACTION PREDICTION AND PROCESSING

• US 20100280882A1
• Filed: 05/04/2010
• Published: 11/04/2010
• Est. Priority Date: 05/04/2009
• Status: Active Grant

• Current Assignee: Visa International Service Association (Visa, Inc.)
• Original Assignee: Visa International Service Association (Visa, Inc.)
• Inventors: Faith, Patrick, Siegel, Kevin P.
• Granted Patent: US 9,489,674 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• 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

Methods, apparatus, and systems are provided for tracking and analyzing data of consumer activity. The tracked data can be organized (e.g. as stored in cache, RAM, hard drives) in certain types of tables, where the tables can be associated with certain tags (keys) for efficiently accessing the data. The organization and associations of the data can also provide simple mechanisms for manipulating the data to obtain results specifically relevant for a task, such as detection of fraud or prediction of consumer behavior to provide better customer service or new services. For example, the tables may contain counters that store the number of times that two correlated consumer events occur within specific time intervals of each other. Such time data can provide efficient determination of patterns of consumer activity.

Claims

1. A method of determining a likelihood of an occurrence of a transaction involving a consumer, the method comprising:

receiving data associated with transactions previously performed by the consumer;

determining a plurality of correlated pairs of the transactions;

providing a plurality of counters, wherein a counter is associated with a time range;

for each correlated pair of transactions;

a computer system determining a time interval between the transactions of the correlated pair; and

the computer system increasing a counter of the plurality of counters, wherein the time interval is within the time range associated with the increased counter; and

using the values of the counters to determine a likelihood of an occurrence of another transaction involving the consumer.

2. The method of claim 1, wherein the likelihood of the occurrence of the another transaction involving the consumer is a likelihood of the another transaction occurring at a particular time.

3. The method of claim 1, wherein the another transaction is a future transaction that has not occurred.

4. The method of claim 1, further comprising:

associating one or more keys with each transaction previously performed by the consumer; and

for each correlated pair of transactions, selecting at least one set of one or more keys that are associated with the correlated pair of transactions, wherein the plurality of counters include a set of counters for each selected set of one or more keys.

5. The method of claim 4, wherein each increased counter is also associated with an amount of at least one of the transactions of the correlated pair, and wherein the counters of a set form a table of various amounts and time ranges.

6. The method of claim 4, wherein a key includes at least a portion of the data associated with a transaction.

7. The method of claim 4, wherein increasing a counter of the plurality of counters for a correlated pair of transactions includes increasing a counter for each set of counters associated with the correlated pair, wherein each increased counter is associated with the time interval for the correlated pair.

8. The method of claim 4, wherein the counters of a set are associated with various time ranges.

9. The method of claim 8, wherein the various time ranges for each set of counters are predetermined.

10. The method of claim 9, wherein each time range corresponds with a frequency of a periodic function that is used to determine the likelihood of the occurrence of the another transaction involving the consumer.

11. The method of claim 9, wherein the time ranges increase in length as the time intervals within the time ranges increase.

12. The method of claim 11, wherein a portion of the counters of a set are respectively associated with time ranges of:

zero to 1 minute, between 1 minute and 2.7 minutes, between 2.7 minutes and 7.4 minutes, between 7.4 minutes and 20 minutes, and over 1.2 million minutes.

13. 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.

14. A computer system comprising:

one or more processors; and

the computer program product of claim 13.

15. A method of determining a likelihood of an occurrence of an event involving a consumer, the method comprising:

receiving data of events associated with the consumer;

associating one or more keys with each event;

determining a plurality of correlated pairs of the events;

for each correlated pair of the events, identifying one or more key pairs associated with the correlated pair;

providing a set of counters for each identified key pair, wherein a counter is associated with a time range;

for each correlated pair of events;

a computer system determining a time interval between the two events of the correlated pair; and

the computer system increasing a counter for each key pair associated with the correlated pair of events, wherein the time interval is within the time range associated with the increased counter; and

using the values of the counters to determine a likelihood of an occurrence of another event involving the consumer, the another event associated with one or more keys.

16. The method of claim 15, wherein the another event has already occurred.

17. The method of claim 15, wherein each set of counters is stored in a table associated with a respective key pair.

18. The method of claim 15, wherein the events are associated with an amount, and wherein the tables have amount ranges on a first axis and time ranges on a second axis.

19. The method of claim 15, wherein the key pair tables are associated with an initial key for an initial event and a final key for the event that occurs later in time.

20. The method of claim 19, wherein using the values of the counters to determine the likelihood of the occurrence of the another event involving the consumer includes:

identifying one or more keys associated with the another event;

selecting the sets of counters for at least a portion of the key pairs having a final key that matches one of the keys associated with the another event; and

using the selected sets of counters to determine the likelihood of the occurrence of the of the another event involving the consumer.

21. The method of claim 20, wherein using the selected sets of counters to determine the likelihood of the occurrence of the of the another event involving the consumer includes:

for each of the at least a portion of the key pairs having a matching final key;

determining a time interval between the another event and an event associated the initial key of the key pair;

using the determined time interval to identify a counter from the respective set of counters, the identified counter associated with a time range that includes the determined time interval.

22. The method of claim 21, further comprising:

summing the values of the identified counters.

23. The method of claim 20, further comprising:

matching a key associated with the another event to the final keys for the at least a portion of the key pairs, wherein matching the key includes aligning the key by broadening key elements of the key until a matching final key is found.

24. The method of claim 19, wherein using the values of the counters to determine the likelihood of the occurrence of the another event involving the consumer includes:

identifying one or more keys associated with the another event;

selecting the sets of counters for at least a portion of the key pairs having an initial key that matches one of the keys associated with the another event; and

using the selected sets of counters to determine the likelihood of the occurrence of the another event involving the consumer.

25. The method of claim 15, wherein an event includes a beginning of a time period.

26. The method of claim 15, wherein an event includes a financial transaction.

27. 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 15.

28. A computer system comprising:

one or more processors; and

the computer program product of claim 27.