Artificial intelligence in financial risk management empirical evidence from Morocco and North Africa
This study investigates the impact of artificial intelligence (AI) adoption on financial risk management performance across 142 banking and financial institutions in five North African countries—Morocco, Egypt, Tunisia, Algeria, and Libya—over 2015–2024. Despite the global surge in AI-driven financial research, the MENA region remains virtually absent from the empirical literature, and existing studies rarely examine the institutional conditions that mediate AI's performance effects in structurally constrained emerging markets. Drawing on an integrated theoretical framework combining the Diffusion of Innovation theory [ 41 ], the Technology Acceptance Model [ 19 ], and Agency Theory [ 30 ], the research adopts a hypothetico-deductive approach implemented through a rigorous seven-step multivariate Ordinary Least Squares (OLS) regression protocol. Five research hypotheses are tested examining the joint effects of AI adoption, data quality, risk governance maturity, regulatory compliance, and institution size on a composite risk management performance score. All classical OLS assumptions are formally verified—anchored in data quality, governance maturity, and regulatory compliance—as the primary determinants of AI-driven risk performance in emerging markets, offering actionable insights for regulators, policymakers, and financial institutions across the MENA region. Results confirm a dominant and statistically significant positive impact of AI adoption on financial risk management performance, mediated by data quality and governance maturity, and amplified by the regulatory framework. Institution size yields no significant direct effect. The model achieves substantial explanatory power, confirming the robustness of the specification. These findings introduce the AI Performance Enabling Ecosystem (APEE) framework—anchored in data quality, governance maturity, and regulatory compliance—as the primary determinants of AI-driven risk performance in emerging markets, offering actionable insights for regulators, policymakers, and financial institutions across the MENA region.
Authors
- Mohamed Makhroute (ORCID: https://orcid.org/0000-0002-3924-8260)
- Abir Attahiri (ORCID: https://orcid.org/0009-0005-1613-3865)
Institutions
- National School of Business and Management in Settat (MA)
- Université Hassan 1er (MA)
Publication Details
- Journal
- Discover Artificial Intelligence
- Published
- 2026-09-21
- DOI
- https://doi.org/10.1007/s44163-026-02233-9
- Primary Topic
- FinTech, Crowdfunding, Digital Finance
- Type
- article
- Field-Weighted Citation Impact
- 0.00