Mining information from credit card time series for timelier fraud detection

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. In the proposed method the patterns inherent in the time series of aggregated daily amounts spent on an individual credit card account has been extracted. These patterns have been used to shorten the time between when a fraud occurs and when it is finally detected, which resulted in timelier fraud detection, improved detection rate and less financial loss.