Two‐stage credit risk prediction framework based on three‐way decisions with automatic threshold learning

Abstract

AbstractCredit risk prediction is a binary classification problem. Using two‐way decisions to classify defaulters may lead to decision errors due to insufficient information. To solve this issue, in addition to identifying borrowers as defaulters and nondefaulters, this paper introduced the delay‐decision mechanism in three‐way decisions, so that records acquiring more information do not make decisions immediately. A two‐stage credit risk prediction framework based on three‐way decisions was proposed to reduce decision risk. In this framework, the decision cost values of three‐way decisions were simplified by analyzing the credit risk prediction, and the expression of threshold calculation was also modified. An optimization objective was built according to the trade‐off between information gain and decision cost, and the particle swarm optimization algorithm was applied to learn the decision thresholds. After adding more supplementary information, the samples in the delayed‐decision region were made further decisions. A dataset from a commercial bank in China was employed to conduct experiments, and the results demonstrated that our proposed method outperformed various base classifiers.