Archive for May, 2012

Build Fraud Detection Model Without Historically Known Fraud Data

Tuesday, May 22nd, 2012

When there are no historically known fraud data, we have to build unsupervised models that do not require a target variable. There are a number of such models that we can try: one-class support vector machine, principal component based compression net, etc. They are detecting data points that are abnormal, not fraudulent per se. We only hope that  being abnormal is highly related to being fraudulent.