A Model of Risk Culture for Artificial Intelligence Adoption in the Banking Industry
Keywords:
Culture, Risk Culture, Artificial Intelligence, Banking Industry, Risk ManagementAbstract
This study aims to develop a comprehensive, systematic, and evidence-based model to explain the components of risk culture in the adoption of artificial intelligence in the banking industry. This qualitative study was conducted using a meta-synthesis approach. The research population included domestic and international studies related to risk culture and artificial intelligence published between 1988 and 2025. A total of 145 studies were initially identified, and after quality appraisal using the CASP tool, 77 sources were selected for final analysis. The synthesis process followed the seven-step method of Sandelowski and Barroso. Data were extracted through open, axial, and selective coding and categorized based on the SIPOC-like framework including context, inputs, processes, and outputs. Inter-coder reliability was confirmed using Cohen’s kappa coefficient (0.635). The findings indicate that AI risk culture in banking is a multidimensional and dynamic construct formed through the interaction of four main dimensions. A total of 18 key concepts and 80 codes were identified across context, input, process, and output dimensions. Contextual factors such as leadership commitment, data governance, and regulatory compliance play a critical role in shaping risk culture. Additionally, data quality, technical standards, and human expertise serve as essential inputs influencing AI effectiveness. At the process level, continuous monitoring, algorithmic transparency, and risk evaluation mechanisms are crucial. These interactions ultimately lead to outcomes such as increased stakeholder trust, improved organizational performance, and enhanced competitive advantage. AI risk culture in the banking industry is not a single-dimensional phenomenon but the result of a complex interaction among organizational, data-related, processual, and outcome-based factors, and effective risk management cannot be achieved without addressing all these dimensions simultaneously.
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