Credit scoring of small and micro enterprises using multi-source information transfer learning

When building credit scoring models for new products or businesses, financial institutions often encounter the “high-dimensional, small samples” problem which results in unsatisfactory model performance. We propose a credit scoring method for small and micro enterprises based on a multi-source transfer learning technique. This method can transfer knowledge from other data sources (source domains) to improve the prediction performance of the credit scoring model of the target domain. Specifically, we first extract the multi-form knowledge from each source domain and then incorporate the information into the building process of the target domain model. The proposed method can take full advantage of the knowledge from source domains, and improve the prediction accuracy of the target domain model. In addition, throughout the modeling process, there is no need to obtain the original data of each source domain, which greatly reduces the risk of privacy leakage during data transmission. Simulation studies and real data analysis illustrate the superior performance of the method on variable selection, estimation, and prediction aspects. This method can effectively transfer the source domain information under privacy-preserving constraints to overcome the “high-dimensional, small samples” problem in the target data set. © 2023 Systems Engineering Society of China. All rights reserved.