THE IMPACT OF ARTIFICIAL INTELLIGENCE-DRIVEN PERSONALIZED BANKING SERVICES ON CUSTOMER TRUST, FINANCIAL INCLUSION, AND CUSTOMER LOYALTY: A CONCEPTUAL STUDY

Abstract

In the banking sector, Artificial Intelligence (AI) is ushering in a new era of personalized services, offering numerous benefits such as improving customer experiences, streamlining operations, and fostering inclusive banking. AI personalised banking services utilise artificial intelligence technologies like machine learning, natural language processing, predictive analytics, intelligent automation and more to deliver customised financial recommendations, product offerings, customer service, fraud detection and data-driven decision making. While existing studies have studied the adoption of AI, digital banking, customer satisfaction and financial technologies in isolation, there is a lack of conceptual studies that focus on the complex relationship between AI-powered personalized banking and customer trust, financial inclusion, and customer loyalty in a single theoretical framework. This conceptual study aims at compiling recent literature published from 2020 to 2025 and creating a framework that gives an understanding of the relationship between these constructs. The proposed framework sets the parameters of AI-powered personalized banking services as the key driver that has a direct impact on customer trust and financial inclusion, thereby improving customer loyalty. The study also suggests that relationships between the above factors are moderated by ethical AI governance, data privacy, transparency, explainability, and digital literacy. The study builds on existing literature in the field of AIpowered banking and draws on several theories, including the Technology Acceptance Model (TAM), Relationship Marketing Theory, Trust Theory and Financial Inclusion Theory to provide a comprehensive understanding of the subject.The study draws upon several theories, including the Technology Acceptance Model (TAM), the Relationship Marketing Theory, the Trust Theory and the Financial Inclusion Theory, to provide a comprehensive understanding of the subject. The conceptual framework being proposed can be used as a basis for further empirical validation by means of the Structural Equation Modeling (SEM) and/or Partial Least Squares Structural Equation Modeling (PLS-SEM).