L-1-penalized fraud detection support vector machines

초록

Standard binary classifiers that maximize the overall accuracy fail in fraud detection where very few fraud cases are concealed within a large number of normal ones, as the best accuracy is often achieved by ignoring all fraud cases. In such a scenario, a natural alternative is what we refer to as a fraud-detection support vector machine, which never fails to detect fraud during training. In this article, we propose the L-1-penalized fraud-detection SVM that is capable of efficiently detecting fraud cases and selecting informative variables simultaneously. We establish the piecewise lin-earity of the L-1-penalized fraud detection SVM as a function of the regularization parameter and then develop an efficient algorithm for computing its entire regulari-zation paths, greatly facilitating its tuning. The advantages of the L-1-penalized fraud detection SVM and its path algorithm for fraud detection are numerically demon-strated using both simulated and real data sets.

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