Examining the integration of generative AI models for improved risk management practices in the financial sector

Abstract

The rapidly evolving landscape of the financial sector demands continuous innovation in risk management practices to ensure stability and sustainability. With the emergence of Generative Artificial Intelligence (GenAI) models like ChatGPT, there's a pivotal opportunity to revolutionise traditional risk management practices. These models possess the capability to analyse vast datasets rapidly, identify emerging risks, and offer predictive insights that surpass human capabilities alone. However, understanding the practical implications and challenges of integrating GenAI into risk management processes is essential for maximising its benefits. Accordingly, this study aims to investigate how GenAI can be effectively employed across different stages of the risk management process, i.e., planning, identification, analysis, response, and monitoring and control, in the financial sector. Drawing on preliminary findings from an open-ended survey of risk management experts in Jordan, the study revealed that GenAI models exhibit significant potential in aiding risk management efforts at each stage. The findings indicate that these models can facilitate more accurate risk identification by capturing complex patterns in financial data, support timely responses to emerging risks, and enhance ongoing monitoring and control mechanisms. However, they also underscore the importance of addressing challenges such as data quality, interpretability, and ethical concerns. This research offers insights into the practical application of GenAI models in risk management, providing actionable guidance for financial institutions in Jordan and beyond. Furthermore, it highlights the importance of continued exploration and adoption of innovative AI technologies to strengthen risk management practices and ensure the resilience of the financial sector in the face of evolving challenges.

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Identifiers

ORCID for Nicholas Dacre: orcid.org/0000-0002-9667-9331

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