Credit Risk Evaluation Framework Based on Heterogeneous Information: Integrating Three-Way Decision and Graph Sample and Aggregate

Abstract

Evaluating credit risk is crucial for financial institutions because it determines whether loans should be granted to applicants, impacting potential returns and losses for the institution. However, modeling the heterogeneous information of applicants while simultaneously balancing institutional gains and losses faced by the institution is a complex challenge. To address the issues, this study proposes a credit risk evaluation framework with heterogeneous information: combining three-way decision (TWD) theory and graph sample and aggregate (GraphSAGE) learning. This framework (i) integrates a fuzzy similarity relation based on fuzzy theory, constructing a GraphSAGE model for graph-structured data, (ii) combines the results of GraphSAGE with the TWD theory to divide loan applications into three regions: positive, negative, and boundary, and (iii) adds additional information for instances of the boundary for final evaluations, providing new perspectives for research in credit risk evaluation. The experiment results demonstrate the excellent effectiveness of the method in credit risk evaluation, coping well with data imbalances while balancing losses and gains.