DATA MINING:
Abstract:
Credit Card Transactions continue to grow in number, taking an ever-larger share of the country’s 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 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 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 address these three main issues concurrently.
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.
