A new user-based model for credit card fraud detection based on artificial immune system

In this paper we present a new model based on Artificial Immune System for credit card fraud detection. In this model, which is based on Artificial Immune Recognition System, user behavior is considered. The model puts together the two methodologies of fraud detection, namely tracking account behavior and general thresholding. The system generates normal memory cells using each user's transaction records, yet fraud memory cells are generated based on all fraudulent records. To get more accurate results, we have performed analysis on training data in order to control the number of memory cells. During the test phase each user's transaction is presented to his/her own normal memory cells, together with fraud memory cells.