NLP-Enhanced Detection of Wrong-Way Risk Contagion Patterns in Interbank Networks: A Deep Learning Approach

This paper presents a novel framework for detecting wrong-way risk (WWR) contagion patterns in interbank networks using natural language processing (NLP) enhanced deep learning approaches. Traditional WWR detection methods rely primarily on historical correlation patterns and structured financial data, limiting their effectiveness during crisis periods when correlations deviate from historical norms. The proposed methodology integrates sentiment analysis from unstructured financial texts with dynamic network topology modeling and temporal pattern recognition through a hybrid Graph Neural Network-LSTM architecture. Experimental evaluation on data spanning 2010-2023 across 87 global systemically important financial institutions demonstrates significant performance improvements over traditional copula-based models. The framework achieves an AUC-ROC of 0.891 compared to 0.734 for conventional approaches, with average early warning lead times extended by 11.5 days. Multi-head attention mechanisms successfully identify critical contagion pathways during historical crisis events with 82% accuracy. The federated learning implementation enables collaborative model training while preserving institutional data confidentiality. Performance analysis across different market volatility scenarios validates the model's robustness during stress periods when accurate risk assessment becomes simultaneously more valuable and challenging. The framework provides regulators and financial institutions with an explainable AI solution for enhancing systemic stability through early intervention at critical contagion chokepoints.