Ekrem Duman, Ozyegin University, Turkey

Binary classification is about predicting which one of the two class values if more likely for a given instance. To learn such a model it is important to have enough number of examples from both class values. When this is not the case, which is known to be class imbalance problem, building strong predictive models becomes a very challenging task. In this study we pick up one such problem: predicting the bank personnel which might commit fraud (stealing money from customer accounts). For this problem, in order to have a strong enough predictive model, we decided to combine the powers of descriptive and predictive modeling techniques where we developed several descriptive models and used them as an input of a predictive model at the last stage. The results show that our solution approach perform quite well.

Personnel fraud, predictive modeling, banking