UBRMTC: User Behavior Recognition Model With Transaction Character

Abstract

Behavior analysis has been used widely in antifraud transactions. However, existing methods of behavior analysis mainly focus on behavior patterns and do not fully consider the behavior psychology of users in the transaction process, which affects the precise recognition of fraudulent behaviors. It is difficult to recognize fraudulent transactions precisely how to describe the user’s behavioral psychology and integrate the behavioral psychology into the transaction behavior. Thus, this article first proposes the transaction character model based on the user’s cautiousness to reflect the user’s behavioral psychology. This model is built from the user’s historical normal interaction behavior data. Then, a user behavior benchmark is established to reflect the user’s behavior pattern from the user’s historical normal transaction behavior data. To integrate the user’s transaction character and user behavior, the mapping relationship model is built by using the least-squares generalized inverse method. This model is the core of the fraudulent behavior recognition method with transaction characters. Experiments in fraud detection scenarios show that the new method improved the average recognition performance of four fraud detection indicators (recall rate, precision rate, accuracy rate, and F1 value) by 23%. The method also shows that individual psychological character has a great influence on user behavior.