International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 6
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Loan Eligibility Assessment Using Machine Learning

Author(s) Ms. Arfa M H, Ms. Akshatha D K, Ms. Jayaprada Y G, Mr. Manjunath Godi, Dr. S Krishna Anand
Country India
Abstract The Loan Eligibility Assessment System is a data-driven solution developed to automate and streamline the process of evaluating a customer’s eligibility for a loan. Financial institutions traditionally rely on manual assessment methods, which can be time-consuming and influenced by human bias. This project aims to enhance decision making by using machine learning techniques to predict loan approval based on applicant information such as income, employment status, credit history, loan amount, and other financial parameters. The system analyses historical loan data to identify key patterns and relationships that affect loan approval outcomes. By training predictive models, such as Logistic Regression, Decision Trees, or Random Forests, the system learns to distinguish between eligible and non-eligible applicants with improved accuracy. The integration of data pre-processing, feature selection, and model optimization ensures that predictions are both reliable and efficient, supporting institutions in reducing default risks. This model helps banks and financial institutions make faster and more consistent decisions, reducing operational workload while enhancing transparency in the loan approval process. It not only minimizes manual errors but also ensures equitable evaluation based on measurable data rather than subjective judgment.
Field Sociology > Banking / Finance
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-02
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62166
Short DOI https://doi.org/hbdsmb
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