METHOD AND SYSTEM FOR DETECTING FRAUDULENT INTERNET MERCHANTS

• US 20110225076A1
• Filed: 03/09/2010
• Published: 09/15/2011
• Est. Priority Date: 03/09/2010
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Nowka, Andrew John, Ip, Lawrence Poi Heng, Mitchell, Andrew Robert, Wang, Xiaohang, Zhao, Shubin
• Application Number:
  US12/720,082
• Publication Number:
  US 20110225076A1
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: G06Q 20/12 specially adapted for electronic shopping systems
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

Systems and methods for detecting fraudulent merchants using the content of orders completed by the merchants. A fraud detection engine of a fraud detection system generates a fraud detection model using feature data extracted from order content data for known fraudulent and known non-fraudulent merchants. The fraud detection engine executes the model using feature data extracted from order content data for a target merchant to determine a fraud risk associated with the target merchant. If the fraud risk of the merchant is indicative of a fraudulent merchant, the fraud detection system can issue a request to a fraud analyst to review the target merchant further. The results of the fraud analyst'"'"'s review can be used to update the fraud detection model.

Claims

1. A computer program product for detecting a fraudulent merchant, the computer program product comprising:

a computer-readable medium comprising;

computer-executable program code for extracting feature data from a plurality of transactions completed by a merchant, the feature data comprising information associated with one or more products purchased in a transaction;

computer-executable program code for executing a fraud detection model using at least the extracted feature data to determine a risk score for the merchant based on the extracted feature data and a correlation of at least a portion of the extracted feature data with feature data associated with known fraudulent merchants; and

computer-executable program code for identifying the merchant for a further action based on the risk score for the merchant.

2. The computer program product of claim 1, wherein the further action comprises at least one of labeling the merchant as fraudulent, labeling the merchant as non-fraudulent, and issuing a request for the merchant to be reviewed further.

3. The computer program product of claim 1, further comprising computer-executable program code for comparing the risk score to a risk threshold to determine the further action.

4. The computer program product of claim 1, further comprising computer-executable program code for prioritizing a request for review of the merchant with a plurality of merchants based on the risk score of the merchant and risk scores for each of the plurality of merchants.

5. The computer program product of claim 2, further comprising:

computer-executable program code for labeling the merchant as fraudulent if the further review determines that the merchant is fraudulent; and

computer-executable program code for labeling the merchant as non-fraudulent if the further review determines that the merchant is non-fraudulent.

6. The computer program product of claim 5, further comprising computer-executable program code for updating the fraud detection model with the feature data associated with the merchant and the label associated with the merchant.

7. The computer program product of claim 1, wherein the fraud detection model determines a risk probability for each feature extracted and wherein the risk score comprises the sum of each of the risk probabilities.

8. The computer program product of claim 7, wherein the risk probability for each feature is directly proportional to the correlation of that feature with a feature associated with known fraudulent merchants.

9. The computer program product of claim 1, wherein fraud detection model comprises one of a Naï

ve Bayes, Perceptron, Winnow, and Support Vector Machine classifier model.

10. A computer program product for detecting a fraudulent merchant, the computer program product comprising:

a computer-readable medium comprising;

computer-executable program code for extracting feature data from a plurality of transactions completed by a merchant, the feature data comprising information associated with one or more products purchased in a transaction;

computer-executable program code for executing a fraud detection model using at least the extracted feature data to determine a risk score for the merchant based on the extracted feature data and a correlation of at least a portion of the extracted feature data with feature data associated with known fraudulent merchants;

computer-executable program code for determining whether the risk score for the merchant comprises a risk score indicative of a fraudulent merchant; and

computer-executable program code for classifying the merchant as fraudulent based on a determination that the risk score for the merchant comprises a risk score indicative of a fraudulent merchant.

11. The computer program product of claim 10, further comprising computer-executable program code for classifying the merchant as non-fraudulent based on a determination that the risk score for the merchant comprises a risk score indicative of a non-fraudulent merchant.

12. The computer program product of claim 10, wherein the computer-executable program code for determining whether the risk score for the merchant comprises a risk score indicative of a fraudulent merchant comprises computer-executable program code for comparing the risk score for the merchant to a risk threshold, wherein the merchant is classified as fraudulent if the risk score exceeds the risk threshold.

13. The computer program product of claim 10, further comprising computer-executable program code for issuing a request for the merchant to be review further is the merchant comprises a classification of fraudulent.

14. The computer program product of claim 13, further comprising computer-executable program code for prioritizing a request for further review of the merchant with a plurality of merchants classified as fraudulent based on the risk score of the merchant and risk scores for each of the plurality of merchants classified as fraudulent.

15. The computer program product of claim 14, further comprising:

computer-executable program code for associating the merchant with a fraudulent label if the review determines that the merchant is fraudulent; and

computer-executable program code for associating the merchant with a non-fraudulent label if the review determines that the merchant is non-fraudulent.

16. The computer program product of claim 15, further comprising computer-executable program code for updating the fraud detection model with the feature data associated with the merchant and the label associated with the merchant.

17. The computer program product of claim 10, wherein fraud detection model comprises one of a Naï

ve Bayes, Perceptron, Winnow, and Support Vector Machine classifier model.

18. A system for detecting fraudulent merchants, the system comprising:

an online payment processor for receiving transaction data associated with a plurality of transactions completed by each of a plurality of merchants, the transaction data comprising information associated with one or more products purchased in a transaction;

a feature extractor in communication with the online payment processor for extracting feature data from the transaction data; and

a fraud detection engine for;

receiving the extracted feature data from the feature extractor for each merchant;

executing a fraud detection model using at least the extracted feature data to determine a risk score for each merchant based on the extracted feature data for that merchant and a correlation of at least a portion of the extracted feature data for that merchant with feature data associated with known fraudulent merchants; and

identifying each merchant for a further action based on the risk score for the merchant.

19. The system of claim 18, wherein the further action comprises at least one of labeling the merchant as fraudulent, labeling the merchant as non-fraudulent, and issuing a request for the merchant to be review further.

20. The system of claim 19, wherein the fraud detection engine prioritizes the further review for each merchant identified for further review based on the risk score for the merchants.

21. The system of claim 18, wherein the fraud detection model determines a risk probability for each of the extracted features and wherein the risk score comprises the sum of each of the risk probabilities.

22. The system of claim 21, wherein the risk probability for each feature is directly proportional to the correlation of that feature with a feature associated with known fraudulent merchants.

23. The system of claim 18, wherein fraud detection model comprises one of a Naï

ve Bayes, Perceptron, Winnow, and Support Vector Machine classifier model.

24. The system of claim 18, wherein the fraud detection engine filters merchants in good standing with the online payment processor from the execution of the fraud detection model.

25. The system of claim 18, wherein the fraud detection engine executes one or more additional fraud models for detecting fraudulent merchants using one of merchant account information, transaction volume, transaction velocity, credit rating, and customer rating.