ATM Fraud Detection using Behavior Model

This research study relationships between fraud and non-fraud transactions of the ATM. Given the transactions, which are obtained from a commercial bank in Thailand, a behavior of each account in one day such as the total amount withdraw and the number of transactions are extracted. Given the set of transactions in each account, the reference is computed within the specific time-window. The reference value and spread are used for constructing a boundary to define the normal and abnormal behavior. Given an instance of aggregate transactions over certain time window, it is predicted as a fraud if the total amount and the total number of transactions per day lies outside the boundary. Experiments are conducted to compare the accuracy between different aggregation time windows and boundaries with different spreading factors.