Risk Identification of Enterprise Artificial-Intelligence Applications from the Perspective of Socio-Technical System Theory: Evidence from OECD AI Incident Monitor and Representative Cases

DOI

10.2991/978-94-6239-825-2_54How to use a DOI?

Keywords

Artificial Intelligence; Financial Risk; Basel Risk Classification; Socio-Technical System Theory; Operational Resilience

Abstract

Against the backdrop of artificial-intelligence-driven financial digital transformation and improving industrial regulatory rules, the popular application of artificial intelligence in corporate financial accounting, risk-control decision-making and capital-market transactions has given rise to new-form financial risks, rendering traditional governance frameworks inadequate. Taking listed-company artificial-intelligence-based financial risk governance as the research subject, this paper adopts data coding statistics and case-study methods. Drawing on 175 valid samples from the OECD artificial-intelligence financial-risk incident database and positive-negative comparative cases of eight Chinese listed firms, this study applies the Socio-Technical System Theory and the Basel Operational Resilience Framework. It reconstructs a financial-risk system adapted to artificial-intelligence scenarios and establishes a multidimensional collaborative risk-governance mechanism. This research mitigates fragmented research perspectives and insufficient empirical evidence in existing literature, and provides theoretical reference and practical suggestions for Chinese listed companies to guard against AI-related financial risks, advance digital transformation while controlling risks, and improve intelligent-financial governance systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haoshu Lu
PY  - 2026
DA  - 2026/11/10
TI  - Risk Identification of Enterprise Artificial-Intelligence Applications from the Perspective of Socio-Technical System Theory: Evidence from OECD AI Incident Monitor and Representative Cases
BT  - Proceedings of the 2026 5th International Conference on Public Service, Economic Management and Sustainable Development (PESD 2026)
PB  - Atlantis Press
SP  - 549
EP  - 563
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-825-2_54
DO  - 10.2991/978-94-6239-825-2_54
ID  - Lu2026
ER  -