A STUDY OF ARTIFICIAL INTELLIGENCE IN THE CONSUMER BEHAVIOUR SPACE OF THE INDIAN BANKING SYSTEM
The rapid advancement and adoption of artificial intelligence (AI) in the banking sector have led to a paradigm shift in customer service, personalization, and overall banking experience. However, the impact of AI-enabled services on customer behaviour, satisfaction, and loyalty remains an area of interest and warrants further investigation. This study aims to understand the perception of customers towards AI-enabled banking services, identify the level of customer satisfaction with these services, and analyze the impact of AI-driven banking solutions on customer satisfaction, which in turn influences customer loyalty.
A sample of 600 customers was surveyed using a structured questionnaire, which encompassed questions related to their perceptions, experiences, and satisfaction levels with AI-enabled services. The collected data was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with Smart PLS software to identify relationships and correlations between the variables of interest. Through this analysis, the study also aims to explore the factors influencing customer trust in AI-driven banking services, the relationship between customer satisfaction and loyalty, and the challenges and opportunities associated with AI adoption in the banking industry.
The findings of this study provide valuable insights for banks and other financial institutions, helping them to better understand the customer perspective on AI-enabled services. By identifying the aspects of AI-driven banking services that contribute to customer satisfaction and loyalty, banks can better harness the potential of AI technologies to enhance their services, strengthen customer relationships, and maintain a competitive edge in the increasingly technology-driven financial services landscape. Furthermore, this study shed light on the challenges that banks may face in adopting AI technologies, such as data privacy, job displacement, and algorithmic bias, and suggest potential strategies for addressing these challenges while capitalizing on the opportunities presented by AI-driven innovations.