A STUDY ON EDUCATIONAL BANK WITH RESPECTIVE TO BANK OF BARODA

Abstract

Educational banking plays a crucial role in supporting the academic aspirations of students by providing financial aid in the form of student loans. Bank of Baroda, being one of India’s leading public sector banks, offers a range of educational loan schemes to support higher education in India and abroad. This study leverages Machine Learning (ML) and Deep Learning (DL) techniques to analyze, evaluate, and forecast the performance and impact of educational banking services provided by Bank of Baroda. By applying intelligent data-driven approaches, this research aims to extract meaningful insights from loan approval data, repayment patterns, customer satisfaction surveys, and academic success linked to financial assistance. The system developed can assist in predictive analytics, default risk assessment, and policy enhancement, making educational finance more efficient and targeted.This study aims to explore and evaluate the role of Bank of Baroda in providing educational banking services, particularly focusing on educational loan schemes, student financing mechanisms, and the technological advancements that facilitate access to education financing. With the growing demand for higher education and the rising cost of academic programs, the need for structured and accessible financial support has become more critical than ever. Public sector banks like Bank of Baroda play a pivotal role in bridging the gap between students and educational opportunities through tailored financial products.The research examines how effectively Bank of Baroda addresses the financial needs of students through its educational loan offerings, interest rate structures, repayment flexibility, and digital processing of applications. It also investigates customer awareness, satisfaction, and challenges faced in the loan approval process. In addition, the study integrates Machine Learning (ML) and Data Analytics perspectives, where applicable, to demonstrate how predictive models could enhance credit assessment, reduce defaults, and improve loan disbursement efficiency.