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@article{188177,
        author = {Roshni Tamrakar and Shanu Gour},
        title = {Review on Credit Risk Assessment Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {580-588},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188177},
        abstract = {Credit risk assessment remains a core function in financial institutions, directly influencing lending decisions, portfolio stability, and financial inclusion. Traditional statistical models, while foundational, often struggle to capture complex borrower behavior, nonlinear risk patterns, and large-scale heterogeneous data. This study investigates the application of machine learning (ML) algorithms for credit-risk prediction and compares their performance with conventional approaches. A comprehensive review of ensemble learning methods, deep neural networks, survival-based models, and hybrid intelligent systems reveals that ML techniques demonstrate superior predictive accuracy, reduced misclassification rates, and improved early-warning capability. Further, the role of explainable AI, fairness-aware modeling, and privacy-preserving learning is examined to ensure regulatory compliance and ethical lending practices. Findings highlight that while ML enhances credit-risk assessment, considerations such as interpretability, data quality, bias mitigation, and governance frameworks are essential for responsible deployment. Overall, this research underscores the transformative potential of machine-learning-based credit scoring in developing accurate, equitable, and scalable financial-risk systems supporting digital and inclusive finance ecosystems.},
        keywords = {Credit Risk, Machine Learning, Loan Default Prediction, Explainable AI, Financial Technology, Risk Modeling},
        month = {December},
        }