Managing personal data with AI in the banking sector

Abstract

In this study, we explore the integration of Artificial Intelligence (AI) into the management of personal data in the banking sector, focusing on the Moroccan context. It looks at how AI can improve operational efficiency, customer trust, and regulatory compliance, all of which are especially relevant in the context of data protection challenges.

The research employs PLS-SEM to analyze the relationships between operational efficiency, customer trust, regulatory compliance and AI risk management in the banking industry. In this research, a quantitative study was prepared among banking professionals in Morocco.

The results show that customer trust (β = 0.832, p < 0.001) and AI risk management (β = 0.829, p < 0.001) significantly influence AI optimization, highlighting the importance of transparency and robust risk management strategies. However, regulatory compliance exhibits a negligible impact (β = -0.002, not significant), revealing gaps in the Moroccan banking sector.

This study contributes to the literature by demonstrating the central role of customer trust and risk management in AI adoption. It calls for the development of AI-driven compliance solutions to enhance regulatory practices in emerging markets.