DEVELOPING A FRAMEWORK FOR QUESTIONNAIRE DESIGN TO ASSESS THE IMPACT OF AI ON TRUST AND MITIGATING CRIMINAL BREACH OF TRUST IN BANKING

Abstract

The banking sector’s reliance on trust is paramount, underpinning the entire financial system’s stability. However, trust is increasingly threatened by unethical behaviors, such as Criminal Breach of Trust (CBT), which have significant repercussions for financial institutions and their stakeholders. This study develops a comprehensive framework for designing a questionnaire aimed at assessing the impact of Artificial Intelligence (AI) on enhancing trust and mitigating CBT in banking. By integrating concepts from established literature and adapting them to the specific context of AI in banking, this study explores the relationships between individual motivations, organizational culture, AI-driven transparency, and accountability mechanisms. The research model proposes trust enhancement through AI as a mediating variable and regulatory oversight as a moderating variable. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), the study will empirically test these relationships to provide insights into the effectiveness of AI in preventing unethical behaviors in the banking sector. This research contributes to the existing literature by filling a critical gap in understanding the role of AI in enhancing trust and preventing CBT, offering practical implications for banking institutions and regulators.