Published Paper Details:

Artificial Intelligence (AI) is rapidly changing the banking landscape, creating significant opportunities and challenges. On the positive side, AI can make work more efficient, productive and productive, while analyti cs capabilities can also improve risk management through fraud detection and prediction. It provides person alized customer service through 24/7 chatbots and customized financial solutions, and even reaches unserve d customers through AI- powered tools. In addition, artificial intelligence transforms the economy by encouraging innovation and lea ding to the development of new products and services. But concerns continue. Automation can change jobs and lead to greater economic pressure. Ethical consider ations are important because AI algorithms can lead to unethical behavior if not designed properly. Security risks such as cyber attacks can lead to data leaks and financial losses. Over- reliance on technology can reduce people's attention and critical thinking, increasing risk. Additionally, data privacy issues need to be carefully considered. Overall, the impact of intelligence on the banking sector is very diverse. While it has the potential to be a po sitive change, addressing ethical concerns, adhering to strict rules and carefully planning its use are importan t to reduce impact, negative impact and ensure that AI benefits banks and their customers in the future. After the introduction of AI in banking, access to some banks is difficult and people are not willing to take risks in the early stages. The study data was collected from primary and secondary data sources. This study was conducted to find out how the adoption of AI in banks will impact customers and bankers, regardless of whether it actually helps them with their business or transactions. Various hypotheses were developed and evaluated to achieve the goal of a value proposition that benefits customers by simplifying transactions and bankers by reducing workload. We collected data for the study from both primary and secondary aspects of data. There are 112 baseline data collected from clients and 30 samples collected from bankers. For the study, a total of 142 pieces of primary data were collected from the perspectives of customers and bankers. Primary data was collected from the perspective of bankers of Axis Bank, ICICI Bank, Karnataka Bank and HDFC Bank. Secondary data from books, magazines and websites were used to obtain additional information. The statistical tools used for testing are chi-square, correlation, and regression. 222222