Risk assessment for contagion path in complex loan network

Abstract

Small and medium-sized enterprises (SMEs) loans play an essential role in many aspects: including technological innovation, economic development, employment, and peoples livelihood, etc. In order to meet the loan evaluation criteria of commercial banks, many SMEs choose to guarantee each other to obtain loans, thus forming a complex guarantee network. If the borrower defaults on the loan, the risk will be diffused to its guarantors along with the contagion path, which may lead to systemic risk across the loan networks. This has brought severe challenges to the nations financial security and regulation. Thus, accurately rating the contagion path is an urgent task for systematic risk management in the loan network. Therefore, we present a deep learning-based approach to the risk rating of contagion paths in the bank industry. We leverage the graph neural network and attention mechanism on graph-structured loan behavior to learn high-order representations, which do not require handcraft feature engineering. We demonstrate that our approach outperforms the existing baselines with 2%$\\sim$15% improvements in risk rating and 3.5% in the newly constructed path rating problem. The result demonstrates the effectiveness of our proposed approach, which provides an effective method and theory basis for regulatory commissions and financial institutions to monitor systematic risks in networked-guarantee loans.