Deep Learning-Based Identification and Quantitative Analysis of Risk Contagion Pathways in Private Credit Markets

Abstract

The private credit market has experienced unprecedented growth, reaching $1.3 trillion globally, necessitating sophisticated risk assessment methodologies to understand complex contagion mechanisms. This research introduces a novel deep learning framework for identifying and quantifying risk contagion pathways within private credit markets. The proposed methodology integrates multi-task deep learning networks with graph neural networks to capture both tem-poral and structural dependencies in risk propagation. A comprehensive analysis of 25,000 pri-vate credit transactions from 2019-2024 demonstrates the framework's superior performance compared to traditional risk assessment approaches. The multi-task learning component achieves 94.7% accuracy in risk feature extraction, while the graph neural network successfully maps contagion pathways with 92.3% precision. Bayesian optimization enhances model performance by 15.2% through automated hyperparameter tuning. The quantitative analysis reveals three primary contagion channels: direct counterparty exposure (45.3%), sectoral correlation (31.7%), and liquidity-driven transmission (23.0%). Experimental results indicate that the proposed framework reduces false positive rates by 38.4% and improves early warning capabilities by 42.1% compared to conventional methods. The identified risk pathways provide actionable insights for portfolio managers and regulatory authorities, enabling proactive risk mitigation strategies. This research contributes to the advancement of financial technology applications in private markets and establishes a foundation for next-generation risk management systems.