The role of intelligent algorithms in systemic financial risk identification: An empirical study of Chinese banking sector

Ziwei Yu () and Fei Huang ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 3, 552-567

Abstract: 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.

Keywords: Artificial intelligence; Chinese banks; Intelligent algorithms; Risk identification systemic risk. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/5254/1933 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:3:p:552-567:id:5254

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().