Questionnaire-Responded Transaction Approach with SVM for Credit Card Fraud Detection

1. Questionnaire-Responded Transaction Approach with SVM for Credit Card Fraud Detection • Adviser：Tung-Shou Chen （陳同孝） Rong-Chang Chen（陳榮昌） Keh-Chien Ma （馬克艱） • Graduate：Bo-Yang Chen （陳柏仰） • Student： Chun-Wei Chen（陳峻偉） Yu-Ru Yang （楊玉汝） Chih-Ru Lin （林志儒） Chun-Bo Tsai（蔡俊伯） Biing-Hsiu Li（李秉修）

2. Outline • Introduction • Flow chart • Questionnaire-Responded Transaction (QRT) • Support Vector Machines (SVMs) • Results and discussion

3. Introduction • Recently, preventing credit card fraud has long been one of the most important issues. • The good solution is collecting personal transaction data by an online questionnaire system.

4. Flow chart QRT data Data analysis Train data and build up a personalized QRT model by SVM Training data Predict a new transaction to be abnormal or not Yes No Abnormal transaction Normal transaction

5. Questionnaire-responded transaction

6. Questionnaire-responded transaction

7. Questionnaire-responded transaction

8. Support Vector Machines (1/5) • mySVM can be used for pattern recognition, regression and classification. y x Non-linear classification

9. Support Vector Machines (2/5)

10. _ + + _ + + （True positive） + _ _ _ _ （True negative） + Support Vector Machines (3/5)

11. QRT Format (4/5)

12. mySVM Format (5/5)

13. Results and discussion (1/7) • The trend is favorable approach since we can acquire high TN rate if we increase Rn. • Alternatively, TP rate decreases with an increase in Rn.

14. Results and discussion (2/7)

15. Results and discussion (3/7)

16. Results and discussion (4/7) • When Rn is low, it is difficult to obtain a high TN rate. • The over-sampling is to replicate the negative data in the minority class. • Or we can improve the TN rate by adding the negative data.

17. Results and discussion (5/7)

18. Hierarchical SVMs (6/7) Positive Negative ……………… ……………… SVM 1 SVM 2 SVM k Aggregation

19. TN rate Average F(X) Fraud Cost Cost Saved 0.48 - 100 % 0 Base case Majority Voting (more 0.74 - 3.32 28.9 % 71.09 % than 4 negatives) 0.82 - 2.35 21.5 % 78.5 % More than 3 negatives Weighting by TN 0.74 - 2.35 28.9 % 71.09 % rate & Voting Weighting by 6 - fold 0.74 - 1.93 28.9 % 71.09 % & 10 - fold cross validation Weighting by 0.74 - 3.07 28.9 % 71.09 % leave - one - out 0.74 - 2.89 28.9 % 71.09 % Hierarchical SVMs Results and discussion (7/7)