Collaborative Prediction in Anti-Fraud System Over Multiple Credit Loan Platforms

Abstract

Anti-fraud engineering for online credit loan (OCL) platforms is getting more challenging due to the developing specialization of gang fraud. Associations are critical features referring to assessing the credibility of loan applications for OCL fraud prediction. State-of-the-art solutions employ graph-based methods to mine hidden associations among loan applications effectively. They perform well based on the information asymmetry which is guaranteed by the huge advantage of platforms over fraudsters in terms of data quantity and quality at their disposal. The inherent difficulty that can be foreseen is the data isolation caused by mistrust between multiple platforms and data control legislations for privacy preservation. To maintain the advantage owned by the platforms, we design a privacy-preserving distributed graph learning framework that ensures critical association repairs by merging parameter sharing and data sharing. Specially, we propose the association reconstruction mechanism (ARM) that consists of the devised exploration, processing, transmission and utilization schemes to realize data sharing. For parameter sharing, we design a hybrid encryption technique to protect privacy during collaboratively learning graph neural network (GNN) models among different financial client platforms. We conduct the experiments over real-life data from large financial platforms. The results demonstrate the effectiveness and efficiency of our proposed methods.