Machine Learning Classifiers for Credit Risk Analysis




Credit risk, Machine Learning, Naive Bayes classifier, Decision tree, K-Nearest neighboor


The modern world is a place of global commerce. Since globalization became popular, entrepreneurs of small and medium enterprises to large ones have looked up to banks, which have existed in various forms since antiquity, as their pillars of support. The risk of granting loans in various forms has significantly increased as a consequence of this, the businesses face financing difficulties. Credit Risk Analysis is a major aspect of approving the loan application that is done by analyzing different types of data. The goal is to minimize the risk of approving the loan for the Individuals or businesses who might not pay back on time. This research paper addresses this challenge by applying various machine learning classifiers to the German credit risk dataset. By evaluating and comparing the accuracy of these models to identify the most effective classifier for credit risk analysis. Furthermore, it proposes a contributory approach that combines the strengths of multiple classifiers to enhance the decision-making process for loan approvals. By leveraging ensemble learning techniques, such as the Voting Ensemble model, the aim is to improve the accuracy and reliability of credit risk analysis. Additionally, it explores tailored feature engineering techniques that focus on selecting and engineering informative features specific to credit risk analysis.


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Rob Gerritsen, “Loan Risks: A Data Mining Case Study

Frawley, W. J., Piatetsky-Shapiro, G., and Matheus, C. J. (1992). Knowledge discovery in databases: An overview. AI Magazine, 13(3):57.

Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (1999). New support vector algorithms. Neural Computation, 12(5), 1207-1245. DOI:

Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767-2787. DOI:

Nguyen, T. T., Tran, H. T. M., & Pham, H. T. (2018). Credit risk prediction using machine learning techniques: A comparison of three methods. Expert Systems with Applications, 114, 125-135.

Zhao, J., Yan, Y., Li, Q., Li, S., & Li, Y. (2019). A comparative study of machine learning algorithms for credit risk assessment. Journal of Ambient Intelligence and Humanized Computing, 10(1), 275-284.

Chen, M.CHuang, S.H, ‘Credit scoring and rejected instances reassigning through evolutionary computation techniques’, Expert Systems with Applications, Vol. 24(4), pp. 433–441, 2003. DOI:

Dietterich, T.G., ‘Experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization’, Machine Learning, Vol. 40, pp.139–157, 2000. DOI:

Pandey, T.N., Jagadev, A.K., Choudhury, D. and Dehuri, S., ‘Machine learning-based classifiers ensemble for credit risk assessment’, Int. J. Electronic Finance, Vol. 7(3/4), pp.227–249, 2013. DOI:




How to Cite

Sudiksha, P. Nanjundan, and J. P. George, “Machine Learning Classifiers for Credit Risk Analysis”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.

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