A Copula-Based Fraud Detection (CFD) Method for Detecting Evasive Fraud Patterns in a Corporate Mobile Banking Context

Abstract

This paper examines some special fraud patterns called evasive fraud patterns (which are types of evasive fraudulent behaviours) caused by a set of marginal fraud risk factors (i.e. individual fraud risk factors) which have inter-dependencies in the form of negative correlations. These inter-dependencies, if not captured properly, may prevent a bank's fraud detection system from detecting such evasive fraud patterns effectively. Using corporate m-banking (m-banking) as an application context, this paper proposes a novel fraud detection method, the CFD method, to address this open issue. The CFD method measures an aggregated fraud risk value for a given corporate m-banking transaction. Different from existing methods, which typically assume that the different marginal fraud risk factors are independent of each other, the CFD method can capture evasive fraud patterns caused by fraud risk factors that are inter-dependent or independent of each other. Evaluation results using Monte Carlo simulations showed that the CFD method is more effective in detecting evasive fraud patterns than the independence-based aggregation method.