{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Integrating artificial intelligence into risk management frameworks: a mixed-methods analysis of the Palestinian banking sector","author":[{"@type":"Person","name":"Kayed M. Tanbour"},{"@type":"Person","name":"Moufida Ben Saada"},{"@type":"Person","name":"A. Nour"},{"@type":"Person","name":"Narmin Kh. Elnaas"}],"datePublished":"2025","isPartOf":{"@type":"Periodical","name":"Journal of Financial Reporting and Accounting"},"identifier":"https://doi.org/10.1108/jfra-06-2025-0458","description":"\n Purpose\n This study aims to investigate the impact of artificial intelligence (AI) on risk management practices within Palestinian banks, specifically examining its application in credit, market and operational risk domains. The research assesses the extent to which AI enhances risk mitigation effectiveness within the unique economic and regulatory context of Palestine.\n \n \n Design/methodology/approach\n The study used an explanatory sequential mixed-methods design. The initial quantitative phase involved surveying 80 internal auditors, selected via simple random sampling from a population of 95. This was followed by a qualitative phase comprising in-depth interviews with 23 purposively selected participants to contextualize and elaborate on the quantitative findings. Data were analyzed using statistical methods and deductive thematic analysis, guided theoretically by the DeLone and McLean (D&M) IS Success Model (2003).\n \n \n Findings\n Findings demonstrate AI’s effectiveness in enhancing credit and operational risk management through improved decision-making accuracy, process automation and real-time anomaly detection. However, its potential contribution to market risk management is significantly constrained by infrastructural limitations, shortages in specialized expertise and competing strategic priorities, thereby underscoring the critical influence of contextual factors on successful AI adoption.\n \n \n Research limitations/implications\n The study acknowledges certain limitations. Primary reliance on internal auditors, while offering crucial oversight, may not capture the full experiential range; future work could benefit from including risk managers, IT specialists and senior management. The unique Palestinian politico-economic context necessarily limits direct generalizability, though identified themes regarding infrastructure, skills and strategy likely resonate with other emerging economies. Building on this study, future research should explore the longitudinal evolution of AI’s impact as infrastructure and skills develop. Comparative cross-country studies within diverse emerging markets would further elucidate national context influences. Integrating deeper analysis of organizational culture, change management and specific ethical considerations related to AI decision-making in risk management represents another fruitful avenue. Exploring the specific impact of different AI techniques (e.g. machine learning vs deep learning) across risk domains would also yield valuable insights. Such research will deepen the understanding of how AI can be effectively and responsibly leveraged to foster resilient global financial systems.\n \n \n Practical implications\n The findings yield significant practical implications for stakeholders within the Palestinian banking sector and, by extension, for other emerging economies confronting similar challenges. First, AI’s differential impact underscores the imperative for banks to adopt a nuanced, risk-specific integration strategy. For credit and operational risks, where AI is effective, institutions should optimize existing systems and ensure robust governance frameworks upholding transparency, accountability and regulatory compliance. Second, identified infrastructural and human capital deficiencies, pivotal impediments in market risk management, necessitate strategic investment in data infrastructure (especially real-time capabilities) and specialized expertise through training, recruitment and partnerships. Third, regulatory bodies should consider developing adaptive governance frameworks, balancing innovation with financial stability and ethics. Incorporating standards like ISO/IEC 42001:2023, with flexibility for local contexts, can guide responsible AI adoption. Finally, a phased, context-sensitive implementation, aligned with continuous evaluation of system performance and organizational readiness, is advocated over wholesale adoption to enhance long-term success and resilience, empowering leaders to maximize AI’s potential within resource-constrained and volatile environments.\n \n \n Originality/value\n This study advances understanding of AI in finance by providing empirical evidence on its differentiated impact across credit, market and operational risks within the Palestinian banking sector, a context marked by institutional and regulatory challenges. Theoretically, it extends the DeLone and McLean IS Success Model to AI-driven risk management. Practically, it offers actionable guidance on human capital, technological infrastructure and governance, fostering sustainable, context-sensitive AI-enabled risk management in emerging economies.\n ","url":"https://innscience.ca/en/community/literature/7bb00db7-39db-4055-a744-62beb3740a16"}