Deep Learning for Credit Risk Assessment and Default Probability Prediction

Abstract

Deep learning has emerged as a powerful class of techniques for credit risk assessment and default probability prediction, yet its adoption is shaped less by predictive accuracy alone than by the broader socio-technical infrastructure in which models operate. This paper examines deep learning systems for credit risk from a systems perspective, analyzing the structural trade-offs among neural architectures, data governance, interpretability, fairness, operational resilience, and regulatory compliance. It situates deep learning within the historical evolution of credit scoring, from accounting-based discriminant analysis and structural models to ensemble methods and representation learning. The discussion emphasizes that default prediction is not merely a classification task but a decision-making process embedded in institutional policy, customer protection obligations, and financial stability objectives. The paper explores recurrent, residual, and attention-based architectures and their implications for temporal credit data, while also addressing post hoc explanation methods and the continuing debate over interpretable models in high-stakes contexts. It further considers fairness constraints, distributional shift, model monitoring, and the role of regulatory frameworks such as operational risk principles and emerging artificial intelligence legislation. By integrating technical and governance perspectives, the paper argues that sustainable deep learning deployment in credit risk requires systematic attention to data lineage, validation, auditability, and alignment with public policy. The analysis provides a research agenda for building robust, fair, and accountable credit risk systems that can adapt to evolving financial and regulatory environments.