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@article{191675,
        author = {Kanika Chopra and Ekta Tyagi and VIjay Raj and Raju Tyagi},
        title = {Predictive Modeling for Home Loan Default Prediction Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7840-7843},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191675},
        abstract = {Home loan lending constitutes a major portion of retail banking portfolios, exposing financial institutions to substantial credit risk in the event of borrower defaults. Traditional credit assessment techniques, primarily based on statistical scoring models, often lack the ability to capture complex, nonlinear relationships among borrower attributes. With the rapid growth of digital banking and big data, machine learning (ML) algorithms have emerged as powerful tools for predictive analytics in credit risk management.
This research paper presents a comprehensive comparative study of multiple machine learning algorithms—Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—for predicting home loan default risk. The study focuses on data preprocessing techniques, feature selection, model training, and performance evaluation using standard classification metrics. Experimental results reveal that ensemble-based boosting models significantly outperform traditional approaches, achieving higher predictive accuracy and recall. The findings highlight the practical relevance of machine learning models for enhancing decision-making in home loan approvals and reducing non-performing assets (NPAs) in the banking sector.},
        keywords = {Home Loan Default, Credit Risk Management, Machine Learning, Predictive Analytics, XGBoost, Financial Technology},
        month = {January},
        }