Loan Default Analysis with Multiplex Graph Learning

Abstract

Aiming to effectively distinguish loan default in the Mobile Credit Payment Service, industrial efforts mainly attempt to employ conventional classifier with complicated feature engineer for prediction. However, these solutions fail to exploit multiplex relations existed in the financial scenarios and ignore the key intrinsic properties of the loan default detection, i.e., communicability, complementation and induction. To address these issues, we develop a novel attributed multiplex graph based loan default detection approach for effectively integrating multiplex relations in financial scenarios. Considering the complexity of financial scenario, an Attributed Multiplex Graph (AMG) is proposed to jointly model various relations and objects as well as the rich attributes on nodes and edges. We elaborately design relation-specific receptive layers equipped with adaptive breadth function to incorporate important information derived from local structure in each aspect of AMG and stack multiple propagation layer to explore the high-order connectivity information. Furthermore, a relation-specific attention mechanism is adopted to emphasize relevant information during end-to-end training. Extensive experiments conducted on the large-scale real- world dataset verify the effectiveness of the proposed model com- pared with state of arts. Moreover, AMG-DP has also achieved a performance improvement of 9.37% on KS metric in recent months after successful deployment in the Alipay APP.