International Journal For Multidisciplinary Research

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

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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A Study on Credit Risk Assessment in Housing Finance using Machine Learning Techniques

Author(s) Ms. Vishali G, Prof. Dr. Elaiyaraja A
Country India
Abstract This study applies machine learning to assess credit risk in housing finance, predicting loan defaults using three years of Mahindra Housing Finance data. It analyzes borrower characteristics, loan attributes, and financial behaviors. Models like Logistic Regression, Gradient Boosting, XGBoost, and Random Forest are used. SHAP analysis highlights key factors, focusing on interest rates and loan amounts. RNN-based time series analysis identifies early warning signs for defaults.
Keywords Keywords: Credit Risk, Housing Finance, Machine Learning, Loan Defaults, SHAP, RNN, Risk Management.
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-30
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.43331
Short DOI https://doi.org/g9g78p
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