International Journal on Science and Technology

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Machine Learning For Bank Loan Eligibility Prediction:Focus on Home Loan and Education Loan

Author(s) T.Lakshmi Narasimha, T.V.S Chandra Rao, P.S Yashwanth Roy, Dr.A.Vinoth Kumar, Dr.T.Kumanan
Country India
Abstract Machine learning (ML) has emerged as a powerful tool in the financial sector, particularly for predicting outcomes in home loans and education loans. This paper examines the application of ML techniques in assessing loan approval, repayment likelihood, and risk management for these two distinct loan types. For home loans, ML leverages credit scores, income data, and property valuations to enhance decision-making in long-term commitments. In contrast, education loans rely on predictive models of future earning potential, academic performance, and institutional factors to evaluate unsecured lending. By employing supervised and unsupervised learning algorithms—such as Random Forests, neural networks, and clustering—ML improves accuracy, reduces defaults, and personalizes loan terms. Despite challenges like data quality and economic variability, ML offers significant benefits, including operational efficiency and broader financial inclusion. This exploration highlights current practices, comparative differences, and the potential for future advancements in predictive lending.
Keywords Machine Learning, Loan Eligibility ,Prediction Home, Loan Education ,Loan Financial ,Analytics Supervised Learning
Field Sociology > Banking / Finance
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-31
Cite This Machine Learning For Bank Loan Eligibility Prediction:Focus on Home Loan and Education Loan - T.Lakshmi Narasimha, T.V.S Chandra Rao, P.S Yashwanth Roy, Dr.A.Vinoth Kumar, Dr.T.Kumanan - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2810
DOI https://doi.org/10.71097/IJSAT.v16.i1.2810
Short DOI https://doi.org/g9dgp5
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