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@article{199533,
        author = {PIYUSH TARACHAND NANNAWARE and JANVHI SHENDE},
        title = {Study of Credit Risk Management in the Banking Sector Using Predictive Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {16046-16051},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199533},
        abstract = {The paper of research looks at the use of predictive analytics in the management of credit risk in the banking industry. With the growth of volumes of data and the requirement of more precise risk assessment by financial institutions, predictive analytics has grown to be a revolutionary tool in improving the credit decisioning process. This paper examines some of the predictive modelling methods such as the use of logistic regression, random forests, and gradient boosting machines, and compares their features in forecasting loan defaults. Other data sources, early warning systems and the necessary critical balance between model accuracy and interpretability to comply with regulations are also explored in the paper. The evidence indicates that machine learning models are far more accurate in prediction than the conventional statistical techniques, and the combination of behavioural and macroeconomic variables also improves the performance of the models.},
        keywords = {Credit Risk Management, Predictive Analytics, Machine Learning, Banking Sector, Default Prediction.},
        month = {April},
        }