Distributed Data Mining in Credit Card Fraud Detection



CREDIT CARD TRANSACTIONS Continue to grow in number, taking an ever-larger share of the US payment system and leading to a higher rate of stolen account numbers and subsequent losses by banks. Improved fraud detection thus has become essential to maintain the viability of the US payment system.

Banks have used early fraud warning systems for some years. Large-scale data-mining techniques can improve on the state of the art in commercial practice. Scalable techniques to analyze massive amounts of transaction data that efficiently compute fraud detectors in a timely manner is an important problem, especially for e-commerce. Besides scalability and efficiency, the fraud-detection task exhibits technical problems that include skewed distributions of training data and nonuniform cost per error, both of which have not been widely studied in the knowledge-discovery and datamining community.

In this article, we survey and evaluate a number of techniques that address these three main issues concurrently. 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. Our approach 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. The US government estimates that credit cards accounted for approximately US $13 billion in Internet sales during 1998.

This 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. 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 detection systems; recently, these models have been the subject of academic research, especially with respect to ecommerce.

The credit card fraud-detection domain presents a number of challenging issues for data mining:

• There are millions of credit card transactions processed each day. Mining such massive amounts of data requires highly efficient techniques that scale.
• The data are highly skewed—many more transactions are legitimate than fraudulent.
• Typical accuracy-based mining techniques can generate highly accurate fraud






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