Determinants of AI Adoption Intention in Credit Risk Management: Evidence from Moroccan Banks

This research examines the behavioural intention of Moroccan banking professionals to ‎integrate artificial intelligence (AI) into credit risk management. Utilizing an expanded ‎Technology Acceptance Model (TAM), the study integrates five principal constructs: perceived ‎usefulness, ease of use, explainability, trust, and regulatory ambiguity. Data were collected ‎from 131 credit risk professionals and examined by Partial Least Squares Structural Equation ‎Modeling (PLS-SEM). The model explains 62.7% of the variance in behavioural intention. The ‎results show that perceived usefulness is the most important factor in adoption, while ‎explainability, trust, and ease of use were not statistically significant. Regulatory ambiguity ‎showed a significant positive effect on behavioural intention, highlighting the importance of the ‎regulatory environment in shaping AI adoption decisions. These findings indicate that in the ‎Moroccan banking sector, performance value and legal frameworks are prioritized over usability ‎or transparency. The study provides actionable insights for policymakers and financial ‎organizations aiming to facilitate AI integration in environments sensitive to compliance‎.