Artificial intelligence and financial decision making in Indian banks: Adoption, effectiveness, and challenges

Abstract

The Indian banking sector is undergoing a profound transformation driven by Artificial Intelligence (AI), which is increasingly reshaping core financial decision making processes. This study investigates the role of AI in credit appraisal, fraud detection, investment planning, and customer relationship management within Indian banks. Employing a mixed method research design, primary data were gathered through structured questionnaires and semi structured interviews administered to 80 banking professionals across public, private, and foreign sector banks, supplemented by secondary data from RBI publications, academic journals, and industry reports. Descriptive statistics, correlation analysis, multiple regression, and thematic coding were applied to analyze the data. Findings indicate that AI adoption is most advanced in fraud detection and customer service, while credit appraisal and investment decision making exhibit moderate integration. AI significantly enhances decision accuracy, reduces operational time, strengthens risk prediction, and supports strategic planning (Gupta & Sharma, 2021). Credit risk assessment benefits from improved NPA prediction and reduced human bias, while chatbot driven CRM tools improve customer satisfaction and retention. However, implementation is constrained by data quality deficiencies, shortage of skilled personnel, high deployment costs, cybersecurity vulnerabilities, and regulatory compliance pressures. The study recommends the development of AI governance frameworks, targeted workforce training, and scalable data infrastructure to enable responsible and effective AI adoption. Overall, AI is established as a strategic enabler that drives data driven, accurate, and operationally efficient financial decision making in the Indian banking context.