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An Investigation of Credit Card Default Prediction Using Machine Learning Classifiers - Decision Tree and SVM
Subject area: Science,Engineering and Technology · Area of research: Credit Card Default Prediction using ML
DOI: 10.64388/IREV9I10-1716381
Abstract
Due to the global financial crisis and elevated credit risk, default forecasting is essential for all economic sectors. Advanced machine learning techniques have replaced traditional linear models for credit default prediction. Big data risk control algorithms now outperform traditional banking techniques in terms of scalability, speed, and accuracy. Support Vector Machine (SVM) and Decision Tree models are compared in this study utilising the German Credit Dataset, which has 21 features and 1000 cases. Following pre-processing that included outlier treatment and category encoding, SVM outperformed Decision Tree with an accuracy of 80.7% versus 72.6%. Through scalable machine learning solutions, these discoveries allow financial institutions to assist small and medium-sized businesses that were previously underserved by traditional banking.
Keywords
Credit default prediction, SVM, Decision Tree, German Credit Dataset, machine learning, credit risk assessment.
References
[1] Arora, S., Bindra, S., Singh, S., & Nassa, V. K. (2022). Prediction of credit card defaults through data analysis and machine learning techniques. Materials Today: Proceedings, 51, 110-117.
[2] Teng, H. W., & Lee, M. (2019). Estimation procedures of using five alternative machine learning methods for predicting credit card default. Review of Pacific Basin Financial Markets and Policies, 22(03), 1950021.
[3] Sahin, Y., & Duman, E. (2011, March). Detecting credit card fraud by decision trees and support vector machines. In Proceedings of the International Multiconference of Engineers and Computer Scientists (Vol. 1, pp. 1-6).
[4] Moula, F. E., Guotai, C., & Abedin, M. Z. (2017). Credit default prediction modeling: an application of support vector machine. Risk Management, 19, 158-187.
[5] Lakshmi, S. V. S. S., & Kavilla, S. D. (2018). Machine learning for credit card fraud detection system. International Journal of Applied Engineering Research, 13(24), 16819-16824.
How to cite this paper
@article{1716381,
author = {Dr. Sunil Kumar Nahak, Ankita Sahu, Deepak Kumar Patra},
title = {An Investigation of Credit Card Default Prediction Using Machine Learning Classifiers - Decision Tree and SVM},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1581-1586},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716381.pdf},
abstract = {Due to the global financial crisis and elevated credit risk, default forecasting is essential for all economic sectors. Advanced machine learning techniques have replaced traditional linear models for credit default prediction. Big data risk control algorithms now outperform traditional banking techniques in terms of scalability, speed, and accuracy. Support Vector Machine (SVM) and Decision Tree models are compared in this study utilising the German Credit Dataset, which has 21 features and 1000 cases. Following pre-processing that included outlier treatment and category encoding, SVM outperformed Decision Tree with an accuracy of 80.7% versus 72.6%. Through scalable machine learning solutions, these discoveries allow financial institutions to assist small and medium-sized businesses that were previously underserved by traditional banking.},
keywords = {Credit default prediction, SVM, Decision Tree, German Credit Dataset, machine learning, credit risk assessment.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716381}
}