Frequency-based transaction prediction and processing

• US 9,489,674 B2
• Filed: 05/04/2010
• Issued: 11/08/2016
• 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.
• Primary Examiner(s): Goldberg, Ivan R
• Application Number:
  US12/773,770
• Publication Number:
  US 20100280882A1
• Time in Patent Office: 2,380 Days
• Field of Search: None
• US Class Current: 1/1
• 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 second transaction involving a consumer, the method comprising:

receiving, by a computer, data associated with first transactions previously performed by the consumer as received from a plurality of access devices associated with a plurality of merchants at a plurality of locations, wherein each transaction of the first transactions involves authorization request and authorization response messages, and clearing and settlement processes, and wherein the first transactions are stored in a transaction database in communication with the computer;

associating, by the computer, one or more keys with each transaction of the first transactions and the second transaction;

determining, by the computer, a plurality of correlated pairs of the first transactions, wherein a correlated pair of first transactions includes an initial transaction associated with an initial key and a final transaction associated with a final key that occurs after the initial transaction;

for each correlated pair of the first transactions, identifying, by the computer, one or more key pairs associated with the correlated pair;

providing, by the computer, a particular set of counters for a particular key pair having a particular initial key and a particular final key, wherein each counter in the particular set of counters is associated with a different time range, wherein the different time ranges do not overlap, wherein the particular set of counters are stored in a local memory of the computer, the local memory storing a plurality of tables of counters, each table corresponding to a different key pair;

after determining the plurality of correlated pairs of the first transactions, for each correlated pair associated with the particular key pair;

determining, by the computer, a respective time interval between the final transaction and the initial transaction of the correlated pair, wherein the respective time interval depends on the correlated pair of first transactions;

selecting, by the computer, a respective counter of the particular set of counters for the particular key pair having a corresponding time range that includes the respective time interval; and

increasing, by the computer, the respective counter of the particular set of counters for the particular key pair, wherein each counter in the particular set of counters is increased for each correlated pair having the respective time interval within the corresponding time range; and

determining, by the computer, the likelihood of the occurrence of the second transaction involving the consumer by;

querying, by the computer, the plurality of tables stored in the local memory to obtain the particular set of counters of the particular key pair;

determining, by the computer, a second time interval between the second transaction associated with the particular final key of the particular key pair and another transaction associated with the particular initial key of the particular key pair, the other transaction occurring prior to the second transaction;

determining, by the computer, which time range of the different time ranges of the particular set of counters includes the second time interval;

comparing, by the computer, a value of the counter associated with the determined time range with one or more values of one or more other counters of the particular set of counters for the particular key pair; and

determining, by the computer, the likelihood of the occurrence in the determined time range of the second transaction involving the consumer based on the comparison, wherein a higher value for the counter associated with the determined time range relative to the one or more other counters indicates a higher likelihood than a lower value for the counter associated with the determined time range relative to the one or more other counters; and

when the likelihood is higher than a threshold value, either authorizing the second transaction by sending a communication over a network to a device for authorization or sending an incentive to the consumer based on the queried particular set of counters and the determined time range based on the likelihood.

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

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

4. The method of claim 1, further comprising:

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

7. The method of claim 4, wherein increasing the respective counter of the particular set of counters for the particular key pair includes increasing a counter for each set of counters associated with the particular key pair, wherein each increased counter is associated with the time interval for the particular key 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 in the determined time range of the second transaction involving the consumer.

11. The method of claim 9, wherein the various time ranges increase in length as the time intervals within the various 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. The method of claim 1, further comprising:

determining, by the computer, a sum of values of each counter in the particular set of counters for the particular key pair; and

determining, by the computer, a ratio of the value of the counter associated with the time range that includes the second interval divided by the sum of the values of the particular set of counters for the particular key pair.

14. The method of claim 1, further comprising:

ranking, by the computer, the counters in the particular set of counters for the particular key pair based on the values of each of the counters, wherein a high value for the counter associated with the determined time range relative to the one or more other counters of the particular set of counters indicates a higher likelihood of the occurrence of the second transaction.

15. The method of claim 1, wherein when the likelihood is lower than the threshold value:

selecting another counter of the particular set of counters associated with a different time range from the determined time range, and

comparing the value of the another counter associated with the different time range with the one or more values of the one or more other counters of the particular set of counters for the particular key pair to determine a likelihood of the occurrence in the different time range of the second transaction.

16. The method of claim 1, wherein when the likelihood is lower than the threshold value, sending the incentive to the consumer based on the queried particular set of counters and the determined time range to incentivize the second transaction.

17. A computer system comprising:

one or more processors; and

a computer program product comprising a tangible computer readable medium storing a plurality of instructions for controlling one or more processors to perform a method of determining a likelihood of an occurrence of a second transaction involving a consumer, the method comprising;

receiving data associated with first transactions previously performed by the consumer as received from a plurality of access devices associated with a plurality of merchants at a plurality of locations, wherein the first transactions are stored in a transaction database in communication with the computer system;

associating, one or more keys with each transaction of the first transactions and the second transaction;

determining a plurality of correlated pairs of the first transactions, wherein a correlated pair of first transactions include an initial transaction associated with an initial key and a final transaction associated with a final key that occurs after the initial transaction;

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

providing a particular set of counters for a particular key pair having a particular initial key and a particular final key, wherein each counter in the particular set of counters is associated with a different time range, wherein the different time ranges do not overlap, wherein the particular set of counters are stored in a local memory of the computer, the local memory storing a plurality of tables of counters, each table corresponding to a different key pair;

after determining the plurality of correlated pairs of the first transactions, for each correlated pair associated with the particular key pair;

determining a respective time interval between the final transaction and the initial transaction of the correlated pair, wherein the respective time interval depends on the correlated pair of first transactions;

selecting a respective counter of the particular set of counters for the particular key pair having a corresponding time range that includes the respective time interval; and

increasing the respective counter of the particular set of counters for the particular key pair, wherein each counter in the particular set of counters is increased for each correlated pair having the respective time interval within the corresponding time range; and

determining the likelihood of the occurrence of the second transaction involving the consumer by;

querying the plurality of tables stored in the local memory to obtain the particular set of counters of the particular key pair;

determining a second time interval between the second transaction associated with the particular final key of the particular key pair and another transaction associated with the particular initial key of the particular key pair, the other transaction occurring prior to the second transaction;

determining which time range of the different time ranges of the particular set of counters includes the second time interval;

comparing a value of the counter associated with the determined time range with one or more values of one or more other counters of the particular set of counters for the particular key pair; and

determining the likelihood of the occurrence in the determined time range of the second transaction involving the consumer based on the comparison, wherein a higher value for the counter associated with the determined time range relative to the one or more other counters indicates a higher likelihood than a lower value for the counter associated with the determined time range relative to the one or more other counters; and

when the likelihood is higher than a threshold value, either authorizing the second transaction by sending a communication over a network to a device for authorization or sending an incentive to the consumer based on the queried particular set of counters and the determined time range based on the likelihood.

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

receiving, by a computer, data of first events associated with the consumer as received from a plurality of access devices associated with a plurality of merchants at a plurality of locations, wherein the first events are stored in a database in communication with the computer;

associating, by the computer, one or more keys with each first event and the second event;

determining, by the computer, a plurality of correlated pairs of the first events, wherein a correlated pair of first events include an initial event associated with an initial key and a final event associated with a final key that occurs after the initial event;

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

providing, by the computer, a particular set of counters for a particular key pair having a particular initial key and a particular final key, wherein each counter in the particular set of counters is associated with a different time range, wherein the different time ranges do not overlap, wherein the particular set of counters are stored in a local memory of the computer, the local memory storing a plurality of tables of counters, each table corresponding to a different key pair;

after determining the plurality of correlated pairs of the first events, for each correlated pair associated with the particular key pair;

determining, by the computer, a respective time interval between the final event and the initial event of the correlated pair, wherein the respective time interval depends on the correlated pair of first events;

selecting, by the computer, a respective counter of the particular set of counts for the particular key pair having a corresponding time range that includes the respective time interval;

increasing, by the computer, the respective counter of the particular set of counters for the particular key pair, wherein each counter in the particular set of counters is increased for each correlated pair having the respective time interval within the corresponding time range; and

determining, by the computer, the likelihood of the occurrence of the second event involving the consumer by;

querying, by the computer, the plurality of tables stored in the local memory to obtain the particular set of counters of the particular key pair;

determining, by the computer, a second time interval between the second event associated with the particular final key of the particular key pair and another event associated with the particular initial key of the particular key pair, the other event occurring prior to the second event;

determining, by the computer, which time range of the different time ranges of the particular set of counters includes the second time interval;

comparing, by the computer, a value of the counter associated with the determined time range with one or more values of one or more other counters of the particular set of counters for the particular key pair; and

determining, by the computer, the likelihood of the occurrence in the determined time range of the second event involving the consumer based on the comparison, wherein a higher value for the counter associated with the determined time range relative to the one or more other counters indicates a higher likelihood than a lower value for the counter associated with the determined time range relative to the one or more other counters; and

when the likelihood is higher than a threshold value, either authorizing the second event by sending a communication over a network to a device for authorization or sending an incentive to the consumer based on the queried particular set of counters and the determined time range based on the likelihood.

19. The method of claim 18, wherein the second event has already occurred.

20. The method of claim 18, wherein each set of counters is stored in a table associated with a respective key pair.

21. The method of claim 20, wherein the first events are associated with an amount, and wherein the tables have amount ranges on a first axis and time ranges on a second axis.

22. The method of claim 20, wherein each of the key pair tables is associated with an initial key associated with the initial events used to determine the counters of the respective key pair table and a final key associated with the final events used to determine the counters of the respective key pair table.

23. The method of claim 22, further comprising:

identifying a group of the key pairs associated with a first key, wherein the second event is associated with the first key, and wherein identifying the group of the key pairs associated with the first key includes;

identifying a group of one or more key pairs having a final key that matches the first key;

selecting the sets of counters of the identified group of the key pairs; and

using the selected sets of counters to determine the likelihood of the occurrence in the determined time range of the second event involving the consumer.

24. The method of claim 23, wherein using the selected sets of counters to determine the likelihood of the occurrence in the determined time range of the second event involving the consumer includes:

for each of the identified group of key pairs;

identifying a third event associated with the initial key of the key pair;

determining a respective time interval between the second event and the third event;

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

25. The method of claim 24, further comprising:

summing the values of the identified counters.

26. The method of claim 23, wherein identifying the group of one or more key pairs having the final key that matches the first key includes:

matching the first key associated with the second event to the final keys of the identified group of key pairs, wherein matching the first key includes aligning the first key by broadening key elements of the first key until a match with the final key is found.

27. The method of claim 22, wherein determining the likelihood of the occurrence of the second event involving the consumer includes:

identifying one or more other keys associated with the second event, the one or more other keys including the first key;

identifying a group of one or more key pairs having an initial key that matches the first key;

selecting the sets of counters of the identified group of key pairs; and

using the selected sets of counters to determine the likelihood of the occurrence in the determined time range of the second event involving the consumer.

28. The method of claim 18, wherein at least one of the first events associated with the consumer includes a beginning of a time period.

29. The method of claim 18, wherein at least one of the first events associated with the consumer includes a financial transaction.

30. The method of claim 18, wherein for each identified key pair, time information associated with events for each identified key pair is stored in a transaction history database table.

31. The method of claim 30, wherein one or more patterns of previous events can be determined from the time information for each relevant identified key pair.

32. The method of claim 20, wherein the key pair tables are generated using the keys and time information of the first events.

33. A computer system comprising:

one or more processors; and

a computer program product comprising a tangible computer readable medium storing a plurality of instructions for controlling one or more processors to perform a method of determining a likelihood of an occurrence of a second event involving a consumer, the method comprising;

receiving data of first events associated with the consumer as received from a plurality of access devices associated with a plurality of merchants at a plurality of locations, wherein the first events are stored in a database in communication with the computer system;

associating one or more keys with each first event and the second event,

determining a plurality of correlated pairs of the first events, wherein a correlated pair of first events include an initial event associated with an initial key and a final event associated with a final key that occurs after the initial event;

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

providing a particular set of counters for a particular key pair having a particular initial key and a particular final key, wherein each counter in the particular set of counters is associated with a different time range, wherein the different time ranges do not overlap, wherein the particular set of counters are stored in a local memory of the computer, the local memory storing a plurality of tables of counters, each table corresponding to a different key pair;

after determining the plurality of correlated pairs of the first events, for each correlated pair associated with the particular key pair;

determining a respective time interval between the final event and the initial event of the correlated pair, wherein the respective time interval depends on the correlated pair of first events;

selecting, by the computer, a respective counter of the particular set of counts for the particular key pair having a corresponding time range that includes the respective time interval;

increasing the respective counter of the particular set of counters for the particular key pair, wherein each counter in the particular set of counters is increased for each correlated pair having the respective time interval within the corresponding time range;

determining the likelihood of the occurrence of the second event involving the consumer by;

querying the plurality of tables stored in the local memory to obtain the particular set of counters of the particular key pair;

determining a second time interval between the second event associated with the particular final key of the particular key pair and another event associated with the particular initial key of the particular key pair, the other event occurring prior to the second event;

determining which time range of the different time ranges of the particular set of counters includes the second time interval;

comparing a value of the counter associated with the determined time range with one or more values of one or more other counters of the particular set of counters for the particular key pair; and

determining the likelihood of the occurrence in the determined time range of the second event involving the consumer based on the comparison, wherein a higher value for the counter associated with the determined time range relative to the one or more other counters indicates a higher likelihood than a lower value for the counter associated with the determined time range relative to the one or more other counters; and

when the likelihood is higher than a threshold value, either authorizing the second event by sending a communication over a network to a device for authorization or sending an incentive to the consumer based on the queried particular set of counters and the determined time range based on the likelihood.