DOI : https://doi.org/10.62527/comien.1.3.21

Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications

Md Murshid Reja Sweet (1), Md Arif (2), Aftab Uddin (3), Kazi Shaharair Sharif (4), Mazharul Islam Tusher (5), Suniti Devi (6), Md Parvez Ahmed (7), Maniruzzaman Bhuiyan (8), Md Habibur Rahman (9), Abdullah Al Mamun (10), Tauhedur Rahman (11), Md Asaduzzaman (12), Md Jamil Ahmmed (13), Md Ariful Islam Sarkar (14)
(1) Department of Management Science and Quantitative Methods, Gannon University, USA
(2) Department of Management Science and Quantitative Methods, Gannon University, USA
(3) Fox School of Business & Management, Temple University, USA
(4) Department of Computer Science, Oklahoma State University, USA
(5) Department of Computer Science, Monroe College, New Rochelle, New York, USA
(6) Department of Management Science and Quantitative Methods, Gannon University, USA
(7) Master of Science in Information Technology, Washington University of Science and Technology, USA
(8) Satish & Yasmin Gupta College of Business, University of Dallas, Texas, USA
(9) Department of Business Administration, International American University, Los Angeles, California, USA
(10) Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
(11) Dahlkemper School of Business, Gannon University, USA
(12) Department of Business Administration, Northern University, Bangladesh
(13) Department of Project Management and Business Analytics, Francis College, USA
(14) Department of Business Administration, Stamford University Bangladesh
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This study evaluates the effectiveness of statistical and machine learning models in credit risk assessment, comparing traditional methods like logistic regression with advanced techniques such as Decision Trees, SVM, Neural Networks, and GBM. The results demonstrate that Neural Networks and GBM achieve the highest predictive accuracy (0.88 and 0.87, respectively), excelling in capturing complex borrower behaviors. Conversely, logistic regression, though more interpretable, shows a lower accuracy of 0.75, highlighting its limitations. The paper underscores the balance needed between model complexity and interpretability, especially in regulatory settings, and provides practical insights for optimizing credit risk assessment models.

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