Development and validation of a credit risk model based on account and counterparty information
This study presents a credit risk estimation framework that integrates firm-level financial indicators, bank account activity, and counterparty information derived from transaction data. While recent research has applied Graph Neural Networks (GNNs) to incorporate inter-firm relationships, accumulating evidence suggests that tree-based models often outperform deep learning methods for structured financial data. We propose a tree-based approach that selects economically significant counterparties based on the sales-to-deposit ratio and incorporates their financial and account characteristics into the prediction model. Using a nine-year, large-scale dataset of Japanese firms, we conduct a rolling-window evaluation and compare the proposed model with standard machine learning methods and a GNN-based approach. The empirical results demonstrate that the proposed model achieves superior predictive accuracy while maintaining interpretability, highlighting the practical value of combining account information with structured counterparty features in credit risk assessment.