Automated Risk Factor Extraction from Unstructured Loan Documents: An NLP Approach to Credit Default Prediction

Abstract

This paper presents a novel framework for extracting risk factors from unstructured loan documentation using advanced natural language processing techniques to enhance credit default prediction accuracy. Traditional credit risk assessment methodologies primarily rely on structured financial data, neglecting valuable insights embedded within textual information. The proposed approach implements a comprehensive pipeline incorporating specialized document preprocessing techniques, transformer-based text analysis, and multi-modal fusion architecture integrating structured and unstructured data sources. Experimental evaluation conducted on 35,438 loan cases from commercial banking institutions demonstrates significant performance improvements, achieving 91.5% accuracy and 0.942 AUC-ROC, outperforming conventional methods by 3.15-12.5% across evaluation metrics. The model successfully identifies critical risk indicators including liquidity constraints, management quality signals, and operational disruption markers with 8.4 months average lead time before default events. Ablation studies confirm the substantial contribution of text-derived features, accounting for 43.6% of total predictive power. The architecture's explainability mechanisms address regulatory compliance requirements through transparent attribution of risk factors. Implementation challenges and future enhancement strategies are discussed, emphasizing practical applicability in financial institutions. This research contributes to the advancement of credit risk management through effective integration of natural language processing techniques with traditional financial analysis methodologies.