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@article{199242,
        author = {Lohitha Koduru and Thanuja Kayam and Himasree Bathala and KAVETI PENCHALAIAH},
        title = {“Intelligent Credit Risk Assessment and Loan Default Prediction Using Machine Learning”},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15147-15153},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199242},
        abstract = {Digital banking and online lending platforms have grown very quickly, making it much easier to get credit services. However, this also increases the risk of loan defaults. Financial institutions are finding it harder than ever to accurately assess credit risk. This paper introduces a machine learning-based methodology for forecasting loan defaults and examining repayment behavior, with the objective of enhancing decision-making in credit assessments. The suggested system makes use of organized financial information, such as the borrower's income, credit history, job status, and payment history. To improve the quality of the data and the performance of the model, we use data preprocessing methods like cleaning, normalization, and feature engineering. We use standard performance metrics like accuracy, precision, recall, and F1-score to test and compare several supervised learning algorithms, such as Logistic Regression, Support Vector Machines, Naïve Bayes, and Random Forest. The Random Forest model is better than the others at making accurate and reliable predictions, and it does a good job of capturing complicated relationships in financial data. The system also has a user-friendly interface for making real-time predictions, which makes it useful for financial institutions. Also, looking at how borrowers repay their loans can give you useful information about their risk patterns, which can help you come up with proactive risk management plans. This method gives you a scalable, data-driven way to better manage credit risk, lower the number of defaults, and help make smart loan approval decisions},
        keywords = {Loan Default Prediction, Credit Risk Assessment, Machine Learning, Random Forest, Logistic Regression, Support Vector Machine (SVM), Data Preprocessing, Feature Engineering, Financial Analytics, Classification Algorithms},
        month = {April},
        }