Description: Fraud is often a dynamic and challenging problem in credit card lending business. Supervised modeling/segmentation techniques are commonly used in fraud detection to distinguish risky transactions from non-risky transactions. However, these techniques frequently rely on identifying risky behavior at a global level. In this paper, along with the classical approach, a new technique has been studied to improve the behavioral fraud detection capability. The application of this proposed technique enables us to identify risky behavior at the account level.