Research on Risk Contagion and Risk Early Warning of China’s Fintech and Banking Industry from the Perspective of Complex Networks

This study selects daily data from 27 fintech companies and 16 listed commercial banks between January 2015 and December 2024 as research samples. Based on complex network theory, we construct an integrated analytical framework encompassing risk measurement, regime identification, and early warning system construction through HD-TVP-VAR model coupled with the Elastic Net algorithm, MS-AR model, and dynamic Logit model. The findings reveal that the total risk spillover rate between fintech and banking ranges from 73.09% to 95.18%, demonstrating significant time-varying and event-driven characteristics in risk contagion. The risk contagion evolution is characterized by three distinct phases: net risk absorption by the banking sector, bidirectional equilibrium contagion, and net risk dominance by the fintech sector. Joint-stock commercial banks and city commercial banks exhibit higher sensitivity to fintech risks compared to state-owned large commercial banks. Key hubs for risk contagion include institutions like Yinxin Technology and Huaxia Bank, with concentrated risk contagion within industry clusters. The MS-AR model accurately delineates low-, medium-, and high-risk zones, showing strong alignment between high-risk periods and major events. The dynamic Logit model incorporating total risk correlation indices demonstrates high consistency between early warning signals and risk evolution trajectories, providing theoretical and practical references for cross-industry systemic financial risk prevention.