Explainable Fraud Detection for Few Labeled Time Series Data

Abstract

Fraud detection technology is an important method to ensure financial security. It is necessary to develop explainable fraud detection methods to express significant causality for participants in the transaction. The main contribution of our work is to propose an explainable classification method in the framework of multiple instance learning (MIL), which incorporates the AP clustering method in the self-training LSTM model to obtain a clear explanation. Based on a real-world dataset and a simulated dataset, we conducted two comparative studies to evaluate the effectiveness of the proposed method. Experimental results show that our proposed method achieves the similar predictive performance as the state-of-art method, while our method can generate clear causal explanations for a few labeled time series data. The significance of the research work is that financial institutions can use this method to efficiently identify fraudulent behaviors and easily give reasons for rejecting transactions so as to reduce fraud losses and management costs.