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@article{207109,
        author = {Aman Gautam and Saijalpreet Kaur and Ashutosh Rauniyar and Akhil Pandey},
        title = {Comparative Study of Traditional And ML-Based Credit Scoring Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {64-68},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207109},
        abstract = {Credit scoring is a crucial component of lending decisions in financial institutions, as it helps banks, credit agencies, and other lenders evaluate the probability of borrower default before approving loans or credit facilities. An effective credit scoring system supports better risk assessment, reduces financial losses, and improves the overall quality of credit management. Traditional credit scoring models have been widely used for several decades because of their simplicity, transparency, interpretability, and acceptance within regulatory frameworks. These models, such as logistic regression, discriminant analysis, and scorecard-based approaches, allow decision-makers to understand how different borrower characteristics influence creditworthiness.
However, with the rapid growth of digital financial services, large-scale customer data, and complex borrowing patterns, traditional models often face limitations in capturing non-linear relationships and hidden patterns in credit data. In this context, Machine Learning (ML) techniques are increasingly being adopted for credit scoring because they can process high-dimensional datasets, handle complex variable interactions, and often provide better predictive accuracy than conventional statistical methods. ML models such as Random Forest, Support Vector Machine, XGBoost, and Artificial Neural Networks have shown strong potential in improving default prediction and credit risk classification.
This paper presents a comprehensive review of traditional and ML-based credit scoring models. It examines their basic methodologies, advantages, limitations, interpretability, regulatory relevance, and comparative performance. The review also discusses recent developments, including hybrid credit scoring models and explainable artificial intelligence, which aim to balance prediction accuracy with transparency. Finally, the paper highlights major insights and future research directions for developing more reliable, fair, and explainable credit risk assessment practices in modern financial systems.},
        keywords = {Credit scoring; Explainable artificial intelligence; Hybrid models; Logistic regression; Machine learning; Random Forest; XGBoost.},
        month = {July},
        }