AGGREGATE MERCHANT MONITORING

• US 20140108209A1
• Filed: 10/16/2012
• Published: 04/17/2014
• Est. Priority Date: 10/16/2012
• Status: Abandoned Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Lo Faro, Walter F.
• Application Number:
  US13/652,909
• Publication Number:
  US 20140108209A1
• Time in Patent Office: Days
• US Class Current: 705/30
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

A method of monitoring cashless transaction data includes extracting transaction history data for a merchant within a first predetermined time period from a transaction data storage. A plurality of forecast models that forecast transaction patterns for the merchant over the first predetermined time period are provided, each forecast model of the first plurality having a different forecast period. The forecast models may be constructed according to a Holt-Winters time-series forecast. The forecast model which most accurately forecasts periodic fluctuations in the transaction history data is selected, and a first forecast of transactions for the merchant for a first forecast period outside the first predetermined time period is provided. A score comparing the forecast with actual data for the first forecast period is further provided. An alert is generated when the score exceeds a predetermined threshold. Also disclosed are a computer and recording medium to carry out the method.

Claims

1. A method of monitoring cashless transaction data, the method comprising:

extracting transaction history data for a merchant within a first predetermined time period from a transaction data storage;

providing a first plurality of forecast models that forecast transaction patterns for the merchant over the first predetermined time period, each forecast model of the first plurality having a different forecast period;

selecting the forecast model which most accurately forecasts periodic fluctuations in the transaction history data;

using the selected forecast model, providing a first forecast of transactions for the merchant for a first forecast period outside the first predetermined time period;

providing a score comparing the provided forecast with actual transaction data from the merchant for the first forecast period; and

generating an alert in response to the score exceeds a predetermined first threshold.

2. The method according to claim 1, further comprising:

extracting transaction history data for a merchant within a second predetermined time period from the transaction data storage; and

providing a second plurality of forecast models that forecast transaction patterns for the merchant over the second predetermined time period, each forecast model of the first plurality having a different forecast period,

wherein selecting the forecast model which most accurately forecasts periodic fluctuations in the transaction history data comprises selecting from the first plurality of forecast models and the second plurality of forecast models.

3. The method according to claim 1, wherein providing a first plurality of forecast models comprises constructing a Holt-Winters time-series forecast for each forecast period.

4. The method according to claim 1, wherein selecting the forecast model which most accurately forecasts periodic fluctuations in the transaction history data comprises selecting the forecast model having a greatest coefficient of determination with respect to the transaction history data.

5. The method according to claim 1, wherein providing a score comparing the provided forecast with actual transaction data from the merchant for the first forecast period comprises providing a standard score scaled to a calculated standard deviation of the transaction history data.

6. The method according to claim 1, further comprising:

forcing the score value to an arbitrary value greater than the first threshold responsive to the existence of a predetermines exception criteria.

7. The method according to claim 1, further comprising:

using the selected forecast model, providing a second forecast of transactions for the merchant in a second forecast period;

using an extended forecast model of transaction patterns for the merchant in the second forecast period, the extended forecast model reflecting the actual transaction data from the first forecast period, providing a third forecast of transactions for the merchant for the second forecast period;

comparing the second and third forecasts; and

generating an alert in response to the second and third forecasts for the second forecast period differ by more than a predetermined second threshold.

8. A non-transitory machine-readable storage medium, having thereon a program of instruction which, when executed by processor, cause the processor to:

extract transaction history data for a merchant within a first predetermined time period from a transaction data storage;

provide a first plurality of forecast models that forecast transaction patterns for the merchant over the first predetermined time period, each forecast model of the first plurality having a different forecast period;

select the forecast model which most accurately forecasts periodic fluctuations in the transaction history data;

using the selected forecast model, provide a first forecast of transactions for the merchant for a first forecast period outside the first predetermined time period;

provide a score comparing the provided forecast with actual transaction data from the merchant for the first forecast period; and

generate an alert in response to the score exceeds a predetermined threshold.

9. The non-transitory machine-readable storage medium according to claim 8, wherein the program of instructions further causes the processor to:

extract transaction history data for a merchant within a second predetermined time period from the transaction data storage; and

provide a second plurality of forecast models that forecast transaction patterns for the merchant over the second predetermined time period, each forecast model of the first plurality having a different forecast period,

wherein the forecast model which most accurately forecasts periodic fluctuations in the transaction history data is selected from the first plurality of forecast models and the second plurality of forecast models.

10. The non-transitory machine-readable storage medium according to claim 8, wherein providing a first plurality of forecast models comprises constructing a Holt-Winters time-series forecast for each forecast period.

11. The non-transitory machine-readable storage medium according to claim 8, wherein selecting the forecast model which most accurately forecasts periodic fluctuations in the transaction history data comprises selecting the forecast model having a greatest coefficient of determination with respect to the transaction history data.

12. The non-transitory machine-readable storage medium according to claim 8, wherein providing a score comparing the provided forecast with actual transaction data from the merchant for the first forecast period comprises providing a standard score scaled to a calculated standard deviation of the transaction history data.

13. The non-transitory machine-readable storage medium according to claim 8, wherein the program of instructions further causes the processor to:

force the score value to an arbitrary value greater than the threshold responsive to the existence of a predetermines exception criteria.

14. The non-transitory machine-readable storage medium according to claim 8, wherein the program of instructions further causes the processor to:

use the selected forecast model to provide a second forecast of transactions for the merchant in a second forecast period;

use an extended forecast model of transaction patterns for the merchant in the second forecast period, the extended forecast model reflecting the actual transaction data from the first forecast period, providing a third forecast of transactions for the merchant for the second forecast period;

compare the second and third forecasts; and

generate an alert in response to the second and third forecasts for the second forecast period differ by more than a predetermined threshold.

15. A system for monitoring cashless transaction data, the system comprising:

a computer including a processing device and a non-transitory, machine-readable storage medium, having thereon a program of instruction which, when executed by processor, cause the processor to;

extract transaction history data for a merchant within a first predetermined time period from a transaction data storage;

provide a first plurality of forecast models that forecast transaction patterns for the merchant over the first predetermined time period, each forecast model of the first plurality having a different forecast period;

select the forecast model which most accurately forecasts periodic fluctuations in the transaction history data;

using the selected forecast model, provide a first forecast of transactions for the merchant for a first forecast period outside the first predetermined time period;

provide a score comparing the provided forecast with actual transaction data from the merchant for the first forecast period; and

generate an alert in response to the score exceeds a predetermined threshold.

16. The system according to claim 15, wherein the program of instructions further causes the processor to:

extract transaction history data for a merchant within a second predetermined time period from the transaction data storage; and

provide a second plurality of forecast models that forecast transaction patterns for the merchant over the second predetermined time period, each forecast model of the first plurality having a different forecast period,

wherein the forecast model which most accurately forecasts periodic fluctuations in the transaction history data is selected from the first plurality of forecast models and the second plurality of forecast models.

17. The system according to claim 15, wherein providing a first plurality of forecast models comprises constructing a Holt-Winters time-series forecast for each forecast period.

18. The system according to claim 15, wherein selecting the forecast model which most accurately forecasts periodic fluctuations in the transaction history data comprises selecting the forecast model having a greatest coefficient of determination with respect to the transaction history data.

19. The system according to claim 15, wherein providing a score comparing the provided forecast with actual transaction data from the merchant for the first forecast period comprises providing a standard score scaled to a calculated standard deviation of the transaction history data.

20. The system according to claim 15, wherein the program of instructions further causes the processor to:

force the score value to an arbitrary value greater than the threshold responsive to the existence of a predetermines exception criteria.

21. The system according to claim 15, wherein the program of instructions further causes the processor to:

use the selected forecast model to provide a second forecast of transactions for the merchant in a second forecast period;

use an extended forecast model of transaction patterns for the merchant in the second forecast period, the extended forecast model reflecting the actual transaction data from the first forecast period, to provide a third forecast of transactions for the merchant for the second forecast period;

compare the second and third forecasts; and

generate an alert in response to the second and third forecasts for the second forecast period differ by more than a predetermined threshold.

22. A method of monitoring cashless transaction data, the method comprising:

extracting transaction history data for a first merchant within a first predetermined time period from a transaction data storage;

identifying a first plurality of customers which exhibit a pattern of recurring transactions with the first merchant;

examining transactions engaged in by the each of first plurality of customers subsequent to the first predetermined time period;

identifying, among the examined transactions, whether there exists a common second merchant with which each of a predetermined portion of the first plurality of customers engaged in at least one recurring transaction with; and

responsive to the existence of the common second merchant, generating an alert indicating a correlation between the first merchant and the second merchant.

23. The method according to claim 22, wherein the first merchant is part of an aggregate merchant, the method further comprising including the second merchant in the aggregate merchant comprising the first merchant.

24. A non-transitory machine-readable storage medium, having thereon a program of instruction which, when executed by processor, cause the processor to extract transaction history data for a first merchant within a first predetermined time period from a transaction data storage;

identify a first plurality of customers which exhibit a pattern of recurring transactions with the first merchant;

examine transactions engaged in by the each of first plurality of customers subsequent to the first predetermined time period;

identify, among the examined transactions, whether there exists a common second merchant with which each of a predetermined portion of the first plurality of customers engaged in at least one recurring transaction with; and

responsive to the existence of the common second merchant, generate an alert indicating a correlation between the first merchant and the second merchant.

25. The non-transitory machine-readable storage medium according to claim 24, wherein the first merchant is part of an aggregate merchant, the program of instruction further causing the processor to include the second merchant in the aggregate merchant comprising the first merchant.