Exploration and prioritization of crucial factors of artificial intelligence adoption in credit risk scoring: using the fuzzy analytical hierarchical process

Purpose This paper aims to investigate and prioritize the critical factors that influence the adoption of artificial intelligence in credit risk assessments and scoring by banks. Design/methodology/approach A systematic literature review was carried out to identify the relevant studies. An exhaustive assessment of these studies enables us to identify criteria and sub-criteria affecting artificial intelligence adoption in banks’ credit risk assessments and scoring. Further, a detailed survey was carried out to consolidate views from various segments, such as academics, bank officers and information technology (IT) managers. Then, a fuzzy analytic hierarchy process (fuzzy-AHP) prioritized critical success factors. Findings The study investigates six main barriers (human, organizational, technological, economic, regulatory and environmental) interlocked with 22 sub-barriers impacting the level of AI adoption in credit risk assessments in the banking sector. The findings indicate that organizational factors (0.227) are the most important, followed by environmental (0.189), regulatory (0.177), economic (0.171), technological (0.138) and human (0.098). Research limitations/implications This study provides a structured understanding of the challenges faced by banks, aiding practitioners in formulating strategies to overcome these barriers. Policymakers and bank executives can leverage these findings to enhance AI implementation and foster innovation in credit evaluation. Originality/value This study fills a critical research gap by employing the fuzzy-AHP methodology to prioritize success factors for AI adoption in credit risk scoring, a novel approach in the banking context. The findings contribute to both academic discourse and practical applications, advancing the integration of AI in financial risk management.