A STUDY ON OPERATION IN BANKING

This study examines the transformative impact of Machine Learning (ML) and Deep Learning (DL) technologies on banking operations, moving beyond traditional manual and rule-based processes. Recognizing the critical need for efficiency, accuracy, and robust risk management in the dynamic Indian banking sector, this research explores how AI-driven solutions are enhancing various operational areas, from front-office customer interactions to complex back-office functions. The study will first establish a baseline of conventional banking operational challenges, including manual data processing, fraud detection limitations, and customer service bottlenecks. It will then delve into the application of ML algorithms, such as Random Forest, Gradient Boosting, and Support Vector Machines, for tasks like predictive analytics in credit scoring, anomaly detection in fraud prevention, and customer segmentation for personalized service. Furthermore, Deep Learning models, including Long ShortTerm Memory (LSTM) networks for sophisticated time-series forecasting (e.g., transaction volumes), and Natural Language Processing (NLP) models (like Transformers for conversational AI), will be explored for automating document processing, enhancing customer support via chatbots, and improving compliance monitoring. The research will also investigate the role of Robotic Process Automation (RPA) as a complementary technology, often integrated with AI, to automate repetitive tasks across various banking workflows. Through a combination of theoretical exploration, practical use cases, and potentially case studies from Indian banks, this study aims to demonstrate how the strategic adoption of ML, DL, and related software solutions leads to significant improvements in operational efficiency, risk mitigation, cost reduction, and ultimately, a more streamlined and customercentric banking experience