A Timelier Credit Card Fraud Detection by Mining Transaction Time Series

As e-commerce sales continue to grow, the associated online fraud remains an attractive source of revenue for fraudsters. These fraudulent activities impose a considerable financial loss to merchants, making online fraud detection a necessity. The problem of fraud detection is concerned with not only capturing the fraudulent activities, but also capturing them as quickly as possible. This timeliness is crucial to decrease financial losses. In this research, a profiling method has been proposed for credit card fraud detection. The focus is on fraud cases which cannot be detected at the transaction level. Based on the fact that there are strong periodic patterns in cardholders' behavior, the time series of aggregated daily amounts spent on an individual credit card has been considered in the proposed method. In this method, the inherent periodic and seasonal patterns are extracted from the time series to construct a cardholder's profile. These patterns have been used to shorten the time between when a fraud occurs and when it is finally detected. Simulation results indicate that the new approach has resulted in a timelier fraud detection, improved detection rate and consequently less financial loss in the cases where a cardholder follows a regular or semi regular periodic behavior. The proposed method is equally applicable to other e-payment methods with minor application-specific modifications.