Generative AI in Operational Risk Management: Harnessing the Future of Finance

Abstract

In an evolving digital landscape marked by escalating operational complexity, the need for innovative operational risk management (ORM) methodologies is more pressing than ever. This thought-leadership paper explores the potential of generative artificial intelligence (AI) – specifically, models such as GPT-4 – to revolutionize ORM practices. With its capacity to analyse vast volumes of unstructured data, simulate risk scenarios and automate labour-intensive tasks, generative AI offers promising opportunities to enhance ORM. Yet, integrating such technology into ORM is not without its challenges, including ensuring data quality and availability, improving model interpretability, validating model performance, managing ethical and privacy concerns, and fostering organizational readiness for change. Despite these hurdles, industry experts anticipate wider application of AI across various risk-management sectors. While reservations about the complexity of AI technologies and regulatory uncertainties persist, the prevailing consensus emphasizes the potential of AI to significantly refine many facets of risk management through automated data analysis. By presenting a balanced exploration of the benefits and challenges associated with generative AI in ORM, the aim of this paper is to guide organizations to effectively leverage this emergent technology. The goal is to equip organizations to better recognize, evaluate and mitigate operational risks in an increasingly intricate and dynamic business environment.

Keywords: generative AI, operational risk management (ORM), banking

JEL Classification: G32, G38, C63, C80, D83, O33