This study examines the underlying drivers of usage intention of chatbots by bank customers using UTAUT model. This model assesses four key factors determining the usage intent of chatbots: performance expectancy, effort expectancy, social influence, and facilitating conditions. Primary data was obtained through structured questionnaire from retail banking customers. Structural Equation Modeling method is used and statistical tool SmartPLS 4.0 was employed to analyze complex associations between variables. The results show that performance expectancy, facilitating conditions, and motivation have a significant association with the usage intention of chatbots; however, effort expectancy and social influence have no connection. In addition, the results show that perceived security and trust are essential criteria for adopting a chatbot in banking. The study contributes value to the existing body of knowledge by proving the factor influencing the intent usage of chatbots in the banking industry. It also highlights future work directions in the form of long-term impacts and insights that can guide banks in designing customer-centric AI systems and improving chatbot services.