MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Abstract

Federated learning has been used extensively in business innovation scenarios in various industries. This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario. First, this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises (MSEs) using multi-dimensional enterprise data and multi-perspective enterprise information. The proposed model includes four main processes: namely encrypted entity alignment, hybrid feature selection, secure multi-party computation, and global model updating. Secondly, a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data, which can provide excellent accuracy and interpretability. In addition, a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model. The results of the study show that the model error rate is reduced by 6.22% and the recall rate is improved by 11.03% compared to the algorithms commonly used in credit risk research, significantly improving the ability to identify defaulters. Finally, the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.