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Credit card transactions continue to grow in number, taking a larger share of the US payment system, and have led to a higher rate of stolen account numbers and subsequent losses by banks. Hence, improved fraud detection has become essential to maintain the viability of the US payment system. Banks have been fielding early fraud warning systems for some years. We seek to improve upon the state-of-the-art in commercial practice via large scale data mining. Scalable techniques to analyze massive amounts of transaction data to compute efficient 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 non-uniform cost per error, both of which have not been widely studied in the knowledge discovery and data mining community. In this article we survey and evaluate a number of techniques that we have proposed and implemented 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.

P. K. Chan, W. Fan, A. L. Prodromidis and S. J. Stolfo, "Distributed Data Mining in Credit Card Fraud Detection," in IEEE Intelligent Systems, vol. 14, no. , pp. 67-74, 1999.
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