Personalized approach based on SVM and ANN for detecting credit card fraud

A novel personalized approach has recently been presented to prevent credit card fraud. This new approach proposes to prevent fraud before initial use of a new card, even users without any real transaction data. This approach shows potential, nevertheless, there are some problems needed solving. A main issue is how to predict accurately with only few data, since it collects quasi-real transaction data via an online questionnaire system and thus respondents are commonly unwilling to spend too much time to reply questionnaires. This study employs both support vector machines (SVM) and artificial neural networks (ANN) to investigate the time-varying fraud problem. The performance of ANN is compared with that from SVM. Results show that SVM and ANN are comparable in training but ANN can have highest training accuracy. However, ANN seems to overfit training data and thus has worse performance of predicting the future data when data number is small.