Adoption of Artificial Intelligence in Banking Services: A Model-Based Analysis of Customer Intention, Satisfaction, and Sectoral Differences

Abstract

The growing integration of Artificial Intelligence (AI) in banking services is reshaping customer experiences, operational processes, and service efficiency within the financial sector. This study proposes and empirically examines a model to analyse the adoption of AI-driven banking services, with particular emphasis on customer behavioural intention and satisfaction. Drawing upon established technology adoption theories, the research identifies key determinants influencing AI adoption, including perceived usefulness, perceived ease of use, trust, perceived risk, and service quality. The study investigates how these dimensions influence customers’ intention to adopt AI-enabled services such as chatbots, automated advisory systems, and intelligent transaction platforms, and how adoption subsequently affects overall customer satisfaction. Furthermore, the research conducts a comparative analysis between public and private sector banks to assess sectoral differences in customer perceptions, adoption patterns, and satisfaction levels. Primary data are collected from banking customers through a structured questionnaire, and advanced statistical techniques, including structural equation modelling, are employed to validate the proposed model and examine the relationships among variables.The findings are expected to contribute to the existing literature by extending technology adoption frameworks within the context of AI-based banking services. Practically, the study provides valuable insights for banking professionals and policymakers to develop effective AI implementation strategies that enhance customer satisfaction, build trust, and strengthen competitive positioning across sectors.