A row-type specific hybrid framework for credit risk analysis: loan portfolio based feature selection and unsupervised Bayesian network dependency exploration

Abstract Credit risk assessment is a critical function in financial analytics, requiring models that can adapt to diverse borrower profiles while providing clear and interpretable insights. Although a range of data driven techniques have been applied in this domain, many struggle to handle the inherent heterogeneity of financial data across different loan categories such as personal and agricultural loans. This paper introduces Credit Risk Analysis with Bayesian Networks (CRAB-Net), a row type specific hybrid framework for credit risk modeling. The approach first segments the data by loan type and balances the distribution of risk categories to ensure fair representation. It then identifies the most outcome relevant attributes through targeted feature selection, focusing on variables most associated with credit risk differentiation. On this refined set of features, unsupervised Bayesian network learning is applied to uncover conditional dependencies among financial variables without relying on default outcome labels. This design combines supervised relevance filtering with unsupervised dependency discovery, reducing noise and avoiding misleading patterns from analyzing all features indiscriminately. The framework revealed that in personal loans, installment-related variables such as installment frequency, overdue status, and repayment structure emerged as central nodes, indicating their dominant role in defining repayment behavior and delinquency risk. In contrast, for agricultural loans, the network structure was shaped primarily by provisioning norms, landholding details, and exposure-related attributes such as sanctioned amount and collateral type, suggesting that borrower risk in this segment is more closely linked to regulatory classification and collateral strength. Experiments on real world banking data show that CRAB-Net provides interpretable dependency graphs, supports fair segment level analysis, and enhances transparency for audit and supervisory compliance under Basel norms by offering clear, data driven evidence of the risk factors shaping borrower outcomes.