Toward Transparent Credit Risk Assessment: A Deep Learning Model with Feature Engineering and Explainable AI

Credit risk prediction with high accuracy and interpretability is of prime importance to financial institutions to avoid defaults and facilitate reliable lending decisions. Conventional machine learning approaches fail to adequately address complex credit risk interactions and lack transparency, a reason for little confidence in critical financial applications. For effective modeling of non-linear financial dynamics in the Credit Risk Dataset, a deep learning architecture reinforced with rich feature engineering and optimal focal loss is proposed to excel in credit risk prediction.A broad range of domain-specific features, incorporating financial ratios, employment stability factors, credit history features, risk-based interaction terms, and multi-level categorical representations, allows the model to learn meaningful borrower profiles and overcome class imbalance problems.For sound convergence and proper generalization, the model is trained using One-Cycle learning rate schedule, gradient clipping, and batch-normalized hidden layers. To improve explainability, SHAP value-based analysis and probability-driven visual interpretation techniques are also integrated to uncover the importance of prominent financial variables like interest rate, loan-to-income ratio, credit defaults, and loan grade. Performance-evaluation on the proposed deep-learning architecture reveals a high validation accuracy of 93.72% and test accuracy of 93.11%, significantly out performing other vanilla ensemble learning methods like Random-Forest and Gradient Boosting in the Credit-Risk Dataset. These results clearly substantiate that an optimized deep-learning solution strengthened with rich financial features and explanation tools can develop accurate , transparent , and practical credit-risk prediction for critical financial applications .