Systems and methods for analysis and linkage between different transaction data providers

• US 9,147,183 B2
• Filed: 03/20/2014
• Issued: 09/29/2015
• Est. Priority Date: 05/12/2010
• Status: Active Grant

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Iannace, Marianne
• Primary Examiner(s): VINCENT, DAVID ROBERT
• Application Number:
  US14/220,512
• Publication Number:
  US 20140207663A1
• Time in Patent Office: 558 Days
• Field of Search: 706/12, 706/45, 706/62
• US Class Current: 1/1
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 20/382 insuring higher security of transaction
• CPC Class Codes: G06Q 20/383 Anonymous user system
• CPC Class Codes: G06Q 20/389 Keeping log of transactions for guaranteeing non-repudiation of a transaction
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Systems, methods, means, computer program code and computerized processes include receiving a first set of de-identified transaction data from a first transaction data source, receiving a second set of de-identified transaction data from a second transaction data source, filtering the first and second sets of de-identified transaction data to identify transactions associated with at least a first entity and to create first and second filtered data sets, removing data associated with an identifier field for each of the transactions in the first filtered data set to created a de-identified first data set, removing data associated with an identifier field for each of the transactions in the second filtered data set to create a de-identified second data set, and processing the first and second de-identified data sets using a probabilistic engine to establish a linkage between data in each data set.

Claims

1. A computerized method, comprising:

receiving a first set of de-identified transaction data from a first transaction data source, the first set of de-identified transaction data having all personally identifiable information removed therefrom;

receiving a second set of de-identified transaction data from a second transaction data source, the second set of de-identified transaction data having all personally identifiable information removed therefrom;

filtering said first and second sets of de-identified transaction data to identify transactions in each of said first and second sets of de-identified transaction data associated with at least a first entity and to create first and second filtered data sets including the identified transactions;

removing data associated with an identifier field for each of said transactions in said first filtered data set to create a de-identified first data set;

removing data associated with an identifier field for each of said transactions in said second filtered data set to create a de-identified second data set; and

processing said first and second de-identified data using a probabilistic engine to establish a linkage between data in each data set, the linkage being a probability score representing a probability match between the first and second de-identified data sets.

2. The method of claim 1, further comprising:

performing a pattern analysis of said data in said first and second filtered data sets to identify a frequency of transactions in said filtered data sets.

3. The method of claim 1, wherein said removing data associated with an identifier field for each of said transactions in said first data set further includes:

creating a first lookup table including said data associated with said identifier field.

4. The method of claim 1, wherein said removing data associated with an identifier field for each of said transactions in said second data set further includes:

creating a second lookup table including said data associated with said identifier field.

5. The method of claim 1, further comprising:

generating at least a first report based on said linkage data using a reporting engine.

6. The method of claim 1, wherein said first transaction data source is a merchant.

7. The method of claim 1, wherein said second transaction data source is a payment network.

8. The method of claim 1, wherein said first transaction data source is a merchant and said filtering includes filtering to identify transactions associated with said merchant.

9. A non-transitory medium having program instructions stored thereon, the medium comprising:

instructions to receive a first set of de-identified transaction data from a first transaction data source, the first set of de-identified transaction data having all personally identifiable information removed therefrom;

instructions to receive a second set of de-identified transaction data from a second transaction data source, the second set of de-identified transaction data having all personally identifiable information removed therefrom;

instructions to filter said first and second sets of de-identified transaction data to identify transactions in each of said first and second sets of de-identified transaction data associated with at least a first entity and to create first and second filtered data sets including the identified transactions;

instructions to remove data associated with an identifier field for each of said transactions in said first filtered data set to create a de-identified first data set;

instructions to remove data associated with an identifier field for each of said transactions in said second filtered data set to create a de-identified second data set; and

instructions to process said first and second de-identified data sets using a probabilistic engine to establish a linkage between data in each data set, the linkage being a probability score representing a probability match between the first and second de-identified data sets.

10. The medium of claim 9, further comprising:

instructions to perform a pattern analysis of said data in said first and second filtered data sets to identify a frequency of transactions in said filtered data sets.

11. The medium of claim 9, wherein said instructions to remove data associated with an identifier field for each of said transactions in said first data set further includes:

instructions to create a first lookup table including said data associated with said identifier field.

12. The medium of claim 9, wherein said instructions to remove data associated with an identifier field for each of said transactions in said second data set further includes:

instructions to create a second lookup table including said data associated with said identifier field.

13. The medium of claim 9, further comprising:

instructions to generate at least a first report based on said linkage data using a reporting engine.

14. The medium of claim 9, wherein said first transaction data source is a merchant.

15. The medium of claim 9, wherein said second transaction data source is a payment network.

16. The medium of claim 9, wherein said first transaction data source is a merchant and said filtering includes filtering to identify transactions associated with said merchant.