Bank

Abstract:
We investigate techniques to improve the cost performance of a bank’s fraud detector by importing remote classifiers from other banks and combining this remotely learned knowledge with locally stored classifiers. The law and competitive concerns restrict banks from sharing information about their customers with other banks. However, they may share black-box fraud-detection models. Our distributed data-mining approach provides a direct and efficient solution to sharing knowledge without sharing data. We also address possible incompatibility of data schemata among different banks. We designed and developed an agent based distributed environment to demonstrate our distributed and parallel data-mining techniques.
Our proposed methods of combining multiple learned fraud detectors under a “cost model” are general and demonstrably useful; our empirical results demonstrate that we can significantly reduce loss due to fraud through distributed data mining of fraud models.
Challenging Issues of credit card fraud-detection domain to Data Mining:
In today’s increasingly electronic society and with the rapid advances of electronic commerce on the Internet, the use of credit cards for purchases has become convenient and necessary. Credit card transactions have become the de facto standard for Internet and Web-based e-commerce. Credit cards accounted for a large amount in Internet sales. Their figure is expected to grow rapidly each year. However, the growing number of credit card transactions provides more opportunity for thieves to steal credit card numbers and subsequently commit fraud. When banks lose money because of credit card fraud, cardholders pay for all of that loss through higher interest rates, higher fees, and reduced benefits. Hence, it is in both the banks’ and the cardholders’ interest to reduce illegitimate use of credit cards by early fraud detection. For many years, the credit card industry has studied computing models for automated...