COMPREHENSIVE STUDY OF INDIAN BANKING SYSTEM

Abstract

The Indian banking system plays a vital role in the economic development of the country by facilitating credit flow, ensuring financial stability, and enabling inclusive growth. From its inception in the colonial era to the liberalization of the 1990s and the recent digital revolution, Indian banking has undergone a series of transformations. Public sector banks, private banks, regional rural banks (RRBs), and cooperative banks form the backbone of the country’s financial ecosystem. However, challenges such as rising non-performing assets (NPAs), regulatory bottlenecks, slow technological adoption, and cyber security threats continue to hinder operational efficiency and customer satisfaction. This comprehensive study explores the structural framework, historical evolution, recent reforms, and operational challenges of the Indian banking system. It also integrates the software domain by evaluating the growing role of Machine Learning (ML) and Artificial Intelligence (AI) in revolutionizing banking operations such as credit risk assessment, fraud detection, customer segmentation, and loan underwriting. Through data collection, case studies, and machine learning model implementation, the research investigates how Indian banks can enhance their efficiency and competitiveness using predictive analytics and intelligent automation. The study concludes by proposing a hybrid roadmap combining traditional banking practices with advanced data-driven solutions for a robust and future-ready Indian banking system