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Despite the growing adoption of artificial intelligence (AI) in the banking industry, limited research has explored its impact on credit risk in Gulf Cooperation Council (GCC) banks. This study addresses this gap by examining the relationship between AI adoption and credit risk, using data from 36 GCC banks between 2015 and 2022. The non-performing loan (NPL) ratio measures credit risk, and a textual analysis of AI-related terms in financial reports proxies AI adoption. Contrary to expectations, the findings reveal a significant positive relationship between AI usage and NPLs, suggesting transitional challenges or potential reporting effects associated with AI integration. These results underscore the need for targeted strategies to optimize AI’s role in credit risk management. Bank executives and policymakers should address challenges related to implementation costs, accountability, and transitional inefficiencies. Enhanced integration of AI into banking processes, alongside robust regulatory frameworks, can improve efficiency and decision-making in credit risk evaluation.

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The Role of Artificial Intelligence in Credit Risk Management: Evidence from GCC Banks

  • 章节

  • 发表日期：

    2026年

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  • Islam Abdeljawad,
  • Walid Esawi,
  • Rashed Abweh,
  • Loai Haddad,
  • Ameer Jaber

摘要

Despite the growing adoption of artificial intelligence (AI) in the banking industry, limited research has explored its impact on credit risk in Gulf Cooperation Council (GCC) banks. This study addresses this gap by examining the relationship between AI adoption and credit risk, using data from 36 GCC banks between 2015 and 2022. The non-performing loan (NPL) ratio measures credit risk, and a textual analysis of AI-related terms in financial reports proxies AI adoption. Contrary to expectations, the findings reveal a significant positive relationship between AI usage and NPLs, suggesting transitional challenges or potential reporting effects associated with AI integration. These results underscore the need for targeted strategies to optimize AI’s role in credit risk management. Bank executives and policymakers should address challenges related to implementation costs, accountability, and transitional inefficiencies. Enhanced integration of AI into banking processes, alongside robust regulatory frameworks, can improve efficiency and decision-making in credit risk evaluation.