Design and Implementation of a Green Credit Risk Control Model Based on SecureBoost and Improved-TCA Algorithm

Green credit plays a crucial role in promoting green transformation of enterprises and advancing social sustainable development. However, the current green credit rating disclosure system lacks data sharing between different institutions, leading to inconsistencies in evaluation results. To address this issue, this study proposes a green credit risk control model based on SecureBoost and an Improved-TCA algorithm. The proposed model combines vertical federated learning result with feature transfer to protect the privacy of participants in different datasets and analyzing the experimental results of vertical federated learning using SHAP values. We proposes improved TCA, which combines the BDA algorithm with the TCA algorithm, and improves the TCA algorithm by setting different weight ratios to comprehensively integrate the advantages of both algorithms to address the issue of significantly different sample distribution quantities in certain data set applications. We proved that the improved TCA algorithm combined with secureBoost has a better prediction result in the multi-classification credit evaluation scenario.