International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Analysis Study Research Paper | Computer Science | India | Volume 13 Issue 2, February 2024


Predictive Power Unleashed: Machine Learning Estimators in Assessing Risky Bank Loans

Deepa Shukla [2]


Abstract: In the evolving landscape of financial risk assessment, traditional econometric models often fall short in addressing the complexity and heterogeneity inherent in bank loan applications. This study introduces a novel application of machine learning (ML) estimators, specifically Gradient Boosting Machines (GBM), to enhance the predictive analytics framework for assessing the risk associated with bank loans. Through a comprehensive analysis of a dataset encompassing various borrower characteristics and loan details, this research aims to demonstrate the superior predictive power of ML models over traditional methods. Employing a robust methodology that includes data preprocessing, feature selection, and model validation, we compared the performance of GBM against traditional logistic regression and other ML models like decision trees, random forests, and neural networks. The evaluation criteria included accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Our findings reveal that GBM outperforms all compared models, showcasing significantly higher accuracy and precision in predicting loan defaults. The model's ability to capture complex, non-linear interactions among predictive variables was highlighted as a key factor in its success. The study's results have profound implications for financial institutions, suggesting that the incorporation of advanced ML techniques into risk assessment processes can lead to more accurate and personalized loan management strategies. Moreover, the identification of critical predictive factors, such as repayment history and debt-to-income ratio, provides valuable insights into the dynamics of loan default risk. In conclusion, "Predictive Power Unleashed: Machine Learning Estimators in Assessing Risky Bank Loans" not only underscores the efficacy of ML models in financial econometrics but also paves the way for future research aimed at refining these models for broader applications in economic analysis and decision-making. By leveraging the capabilities of machine learning, financial institutions can achieve a deeper understanding of risk factors, ultimately contributing to more informed and equitable lending practices.


Keywords: Gradient Boosting Machines, machine learning, financial risk assessment, loan default prediction, predictive analytics


Edition: Volume 13 Issue 2, February 2024,


Pages: 933 - 936


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