Artificial Intelligence-driven financial assistants provide an accessible, responsive and 24/7 service offering insights into spending habits, suggesting savings plans and even resolving queries related to banking tasks. As the banking sector continues to evolve, the integration of AI-driven financial assistants is expected to further transform how banks operate, improving both customer satisfaction and the efficiency of banking services. The adoption of AI-powered financial assistants in the banking sector is revolutionizing how financial services are delivered, offering a combination of convenience, personalized services, and improved operational performance. The usage of the Artificial Intelligence-driven Financial Assistant services in the banking sector is the latest wave of new technologies adopted.
As more customers are inclining towards tech-based services, the aim of the present study is to examine the impact of demographic variable i.e occupation on the Customer's adoption factors of AI-driven financial assistant. The study is based on primary data collected through an adapted questionnaire that contains the six major adoption factors of AI-driven financial assistants such as Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Personal Innovativeness, Trust and Behavioural Intention to use AI-driven financial assistant. The data has been collected from 374 banking customers of Haryana through a survey. ANOVA has been used to identify the findings and the result of the present study indicates that on the basis of occupation, there is significant difference found in customer’s adoption factors of Personal Innovativeness while there is no significant difference found in Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and Trust. In addition to this, behavioural intention to use AIFA also differentiate among different groups on the basis of occupation. AI-driven financial assistant services are transforming the way banks interact with customers and streamline internal processes. The results of the present study will help banks and financial institutions to better understand their customers.