RESOLVING SIMILAR ENTITIES FROM A TRANSACTION DATABASE

• US 20140279299A1
• Filed: 03/14/2013
• Published: 09/18/2014
• Est. Priority Date: 03/14/2013
• Status: Active Grant

• Current Assignee: Palantir Technologies Incorporated
• Original Assignee: Palantir Technologies Incorporated
• Inventors: ERENRICH, Daniel
• Granted Patent: US 10,140,664 B2
• Time in Patent Office: Days
• US Class Current: 705/30
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/10 Tax strategies

Abstract

A technique for identifying related transaction records from a database storing transaction records for multiple entities includes grouping transaction records with a common attribute value into transaction record sets, receiving a selection of an exemplar record set and determining the probability the transaction record set stores transaction records associated with a first entity. Other operations include resolving the transaction record set as storing transaction records associated with the first entity. This improves the process of identifying related transaction records because related transaction records missed by string comparisons transaction record attributes are detected.

Claims

1. A method for identifying related transaction records from a database storing transaction records for multiple entities, the method comprising:

retrieving a plurality of transaction record sets, wherein each transaction record set includes one or more of the transaction records sharing a common attribute value;

receiving a selection of or selecting an exemplar record set, wherein the exemplar record set comprises a plurality of the transaction records associated with a first entity;

for each of the plurality of transaction record sets;

determining a probability that the transaction record set stores transaction records associated with the first entity, and

upon determining the probability exceeds a threshold, resolving the transaction record set as storing transaction records associated with the first entity.

2. The method of claim 1, wherein the financial transaction records are credit or debit transactions processed by a financial institution for a merchant.

3. The method of claim 2, wherein attributes of the financial transaction records include one or more of the following:

an identification of the merchant from which the financial transaction originates;

an identification of the credit or debit account owner;

an amount of the financial transaction;

a date of the financial transaction;

a time of the financial transaction; and

a location of where the financial transaction originated.

4. The method of claim 3, further comprising determining, for each transaction record set, aggregate values for the attributes of the transaction record set;

and

determining aggregate values for attributes of the exemplar record set.

5. The method of claim 1, wherein determining a probability comprises passing a transaction record set and the exemplar record set to a classifier, wherein the classifier is configured to determine the probability that the transaction record set stores transaction records associated with the first entity.

6. The method of claim 5, wherein the classifier is a random forest classifier.

7. The method of claim 5, further comprising, training the classifier using a plurality of training examples, the training examples comprising:

one or more first pairs of transaction record sets, wherein each first pair represent a common entity; and

one or more second pairs of transaction record sets, wherein each second pair represents unrelated entities.

8. The method of claim 5, wherein the classifier evaluates features of each transaction record, including at least one of a word overlap count, word frequency, a word-based or character based cosine similarity, merchant category codes, and numeric city codes associated with each transaction record.

9. The method of claim 5 wherein the classifier evaluates features of each transaction record including a at least one of a fractional difference in size of an average ticket-size in the transaction record, a standard deviation between the average ticket-sizes in the transaction records, and a fractional difference in a magnitude of ticket-size variances

10. The method of claim 1, wherein resolving the transaction record set to the exemplar record set comprises:

merging the transaction records of the transaction record set into the exemplar record set.

11. The method of claim 1, further comprising, performing an analysis on a set of the transaction records, wherein the set includes the transaction records of the exemplar record set and the transaction records resolved as associated with the first entity.

12. A computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform an operation for identifying related transaction records from a database storing transaction records for multiple entities, the method comprising:

retrieving a plurality of transaction record sets, wherein each transaction record set includes one or more of the transaction records sharing a common attribute value;

receiving a selection of or selecting an exemplar record set, wherein the exemplar record set comprises a plurality of the transaction records associated with a first entity;

for each of the plurality of transaction record sets;

determining a probability that the transaction record set stores transaction records associated with the first entity, and

upon determining the probability exceeds a threshold, resolving the transaction record set as storing transaction records associated with the first entity.

13. The computer readable medium of claim 12, wherein the financial transaction records are credit or debit transactions processed by a financial institution for a merchant.

14. The computer readable medium of claim 13, wherein attributes of the financial transaction records include one or more of the following:

an identification of the merchant from which the financial transaction originates;

an identification of the credit or debit account owner;

an amount of the financial transaction;

a date of the financial transaction;

a time of the financial transaction; and

a location of where the financial transaction originated.

15. The computer readable medium of claim 14, further comprising determining, for each transaction record set, aggregate values for the attributes of the transaction record set;

and

determining aggregate values for attributes of the exemplar record set.

16. The computer readable medium of claim 12, wherein determining a probability comprises passing a transaction record set and the exemplar record set to a classifier, wherein the classifier is configured to determine the probability that the transaction record set stores transaction records associated with the first entity.

17. The computer readable medium of claim 16, wherein the classifier is a random forest classifier.

18. The computer readable medium of claim 16, further comprising, training the classifier using a plurality of training examples, the training examples comprising:

one or more first pairs of transaction record sets, wherein each first pair represent a common entity; and

one or more second pairs of transaction record sets, wherein each second pair represents unrelated entities.

19. The computer readable medium of claim 12, wherein resolving the transaction record set to the exemplar record set comprises:

merging the transaction records of the transaction record set into the exemplar record set.

20. The computer readable medium of claim 12, further comprising, performing an analysis on a set of the transaction records, wherein the set includes the transaction records of the exemplar record set and the transaction records resolved as associated with the first entity.

28. The system of claim 19, wherein resolving the transaction record set to the exemplar record set comprises:

merging the transaction records of the transaction record set into the exemplar record set.

29. The system of claim 19, further comprising, performing an analysis on a set of the transaction records, wherein the set includes the transaction records of the exemplar record set and the transaction records resolved as associated with the first entity.

21. A computer system, comprising:

a memory; and

a processor storing one or more programs configured to perform an operation for identifying related transaction records from a database storing transaction records for multiple entities, the method comprising;

retrieving a plurality of transaction record sets, wherein each transaction record set includes one or more of the transaction records sharing a common attribute value;

receiving a selection of or selecting an exemplar record set, wherein the exemplar record set comprises a plurality of the transaction records associated with a first entity;

for each of the plurality of transaction record sets;

determining a probability that the transaction record set stores transaction records associated with the first entity, and

upon determining the probability exceeds a threshold, resolving the transaction record set as storing transaction records associated with the first entity.

22. The system of claim 21, wherein the financial transaction records are credit or debit transactions processed by a financial institution for a merchant.

23. The system of claim 22, wherein attributes of the financial transaction records include one or more of the following:

an identification of the merchant from which the financial transaction originates;

an identification of the credit or debit account owner;

an amount of the financial transaction;

a date of the financial transaction;

a time of the financial transaction; and

a location of where the financial transaction originated.

24. The system of claim 23, further comprising determining, for each transaction record set, aggregate values for the attributes of the transaction record set;

and

determining aggregate values for attributes of the exemplar record set.

25. The system of claim 21, wherein determining a probability comprises passing a transaction record set and the exemplar record set to a classifier, wherein the classifier is configured to determine the probability that the transaction record set stores transaction records associated with the first entity.

26. The system of claim 25, wherein the classifier is a random forest classifier.

27. The system of claim 23, further comprising, training the classifier using a plurality of training examples, the training examples comprising:

one or more first pairs of transaction record sets, wherein each first pair represent a common entity; and

one or more second pairs of transaction record sets, wherein each second pair represents unrelated entities.