Transformed Graph Attention for Credit Rating

Abstract

In banking finance and lending, credit rating is an important tool to measure creditor and debt reliability and prevent systemic risk among banks. Intrinsic features are key factors in risk control decisions. Decision models built with these intrinsic features are often used to assess risk classification and asset safety, but there lacks research on credit prediction for bank rating outlooks with financial correlation, which reflects the structured pattern of systemic risk and contagion. Although correlations among financial entities are valuable for rating outlook, it is hard to integrate such interactions into feature-based solutions. Aiming at the issues above, we propose a hyper-feature mapping operator based on an attention-based knowledge representation to build a knowledge system for the credit rating prediction of the interbank to represent the knowledge of credit rating. Combined with the interrelated behavior graph of each entity, a hyper context graph attention with intrinsic properties (assets, capabilities, buffers) are represented as knowledge for rating prediction to extract the context representation, helping understand the interaction of risk between intrinsic properties and entity-to-entity. Through the experimental comparison and evaluation based on 10 years of actual interbank data, the results show that the proposed model is effective with higher performance compared with the popular rating prediction methods currently used in the industry.

Sheng Xiang

Ph.D. Candidate

My research interests include learning-based graph simulation for data management and spatio-temporal and uncertain data mining.