Analysis of Barriers To Ai Banking Chatbot Adoption In India: An Ism and Micmac Approach

Chatbots are becoming popular in the rapidly developing field of Artificial Intelligence (AI) to facilitate more effective communication between businesses and customers. AI-powered banking chatbots are gaining popularity and present novel opportunities to provide 24/7 front-line support and customised banking assistance. Despite these advantages, banking chatbots are not widely used and have not been adopted as customer service in Indian banks. This research paper explores the obstacles associated with the widespread adoption of banking chatbots in the financial landscape. As disruptive technologies like Artificial Intelligence and Natural Language Processing (NLP) continue to reshape the banking industry globally, understanding the specific barriers to chatbot integration becomes imperative. The current research contributes to the AI discipline by holistically examining the barriers to banking chatbot adoption in India using the Interpretive Structural Modelling (ISM) methodology. The study employs a three-step approach by identifying key barriers to adopting banking chatbots through an extensive literature review and experts' opinions. Then, the Interpretive Structural Modelling (ISM) methodology creates a hierarchical model. For this, data is collected from subject matter experts to develop the interpretive model. Thirdly, MICMAC analysis is conducted to classify and sort the corresponding variables based on their driving and dependence power. The analysis reveals that the absence of AI guidelines, lack of human touch, and lack of audibility and transparency of AI systems are some of the critical barriers to the deployment of AI banking chatbots, requiring special focus to streamline the regulatory framework and anthropomorphic features of AI chatbot for successful implementation and deployment. Recommendations for practitioners and other stakeholders, and research limitations are also discussed.