Data Mining and Fraud Prevention

Business Data Miners proprietary algorithm is able to detect cards that will likely have fraudulent activities within 6 months in the future. If these cards are reissued or closely monitored, we can reduce a significant amount of the fraud loss. In a live testing of our algorithm at a top 15 bank, 19% of cards that we predicted as being compromised had fraudulent activities within 3 months after our prediction. We estimate that the annual fraud savings is above $1 million.  By reissuing risky cards, we prevent fraudulent activity before it happens. An added benefit of doing this is that the impact of fraud on good customers is eliminated.

Feedback from the client, one of the top 15 banks in US, was great. The volume of existing ASI19 check fraud alerts is reduced while more frauds are detected.

Delivered the optimal debit card signature transaction daily limit for a client bank. Performing the similar analysis to optimize PIN-based transaction daily limit. The core idea is to maximize the net of transaction fee plus overdraft fee minus fraud loss. The main technical challenge is to merge 100 million records in settlement, authorization, overdraft and claim fuzzily and iteratively.

About 25% of compromised good cards identified by BDM will have fraudulent activities within the next 3 months. (In comparison, usually about 1% of cards alerted by a popular point of compromise (POC) service from a big company have fraudulent within 12 months.) The common points of compromise detected by BDM are highly accurate. Identifying POC accurately  and early and reissuing cards provide the following benefits: 1. It is better than real time fraud detection because the fraud loss can be stopped before it happens; 2. Reissuing cards  is less intrusive to good customers than blocking suspicious transactions.