The role of intelligent algorithms in systemic financial risk identification: An empirical study of Chinese banking sector - Yu, Ziwei; Huang, Fei (Edelweiss Applied Science and Technology, 2025)
This study investigates how intelligent algorithms enhance systemic financial risk identification in Chinese banking, addressing the gap between technological capabilities and risk management applications. We analyze 36 listed commercial banks from 2018 to 2023 using instrumental variable estimation and difference-in-differences analysis, with comprehensive measures for AI implementation and systemic risk (CoVaR, MES, SRISK). The implementation of intelligent algorithms significantly reduces systemic risk exposure by 18.5%. The primary mechanisms include risk identification efficiency (42.3%), information processing capacity (35.7%), and decision-making optimization (22.0%). Larger banks demonstrate stronger risk-reduction benefits from AI implementation. The research confirms that intelligent algorithms substantially enhance banks’ ability to identify and manage systemic risk through multiple operational channels, with effects varying across bank characteristics and market conditions. Regulators should encourage AI adoption for risk management, particularly among smaller institutions, while developing standardized frameworks for evaluating AI-based systems. The heterogeneous effects across bank types suggest the need for tailored technological implementation approaches in risk management.