Explainable Deep Learning for Credit Risk Assessment: A Data-Driven Decision Support Framework for Financial Institutions

Credit risk assessment is a critical job for financial institutions, as it can affect credit loss, restrict profitable lending opportunities, and raise issues for the institutions related to their operations and/or reputations. Though traditional statistical credit-scoring models are easily understood, they might not be capable of fully revealing complex non-linear interactions among borrower data. It is therefore desirable to explore a different approach to learning the nonlinear interactions in multidimensional credit data, and deep learning is an alternative that has been suggested; it remains a challenge, however, as it is non-textual, which gives rise to issues of managerial acceptability and responsibility, transparency, and justice. This study has incorporated three components of explainable deep learning decision support systems: A multilayer neural network, probability calibration, and Shapley Additive exPlanations (SHAP), along with risk-based decision routing and fairness monitoring. The proof-of-concept study was based on a synthetic population of 30,000 credit applicants who have 19 financial and behavioral variables. The proposed deep neural network achieved an accuracy of 0.9891, an ROC-AUC value of 0.9965, a balanced accuracy of 0.9886, a recall score of 0.9875, and an F1 score of 0.9784. Platt calibration improved its Brier score from 0.0106 to 0.0068 without decreasing discriminating ability. The most significant worldwide risk variables were found by the SHAP study to be recent delinquencies, missing payments, debt-to-income ratio, credit utilization, and length of credit history. Examples presented throughout this study demonstrate that all of these can be integrated into a single decision support system, which includes: predictive modelling, explanation, calibration, human oversight, and fairness audits. The proposed approach does not aim to replace credit officers but rather to give financial institutions clear and transparent evidence of risk that supports a consistent and operationally able lending process.