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A Comparative Study of Machine Learning Algorithms for Enhanced Credit Default Prediction

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Evolutionary Artificial Intelligence (ICEASSM 2017)

Abstract

In today’s competitive financial arena, accurate credit default prediction is very important for sustaining the stability and profitability of banks. This research study presents a comparative analysis of various machine learning algorithms, which are used for forecasting the likelihood of credit default. Six diverse algorithms—Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Logistic Regression, Decision Tree (DT), Gaussian Naive Bayes, and Random Forest (RF)—are used to construct the predictive comparison. All the models were trained and evaluated by using an investment dataset obtained from a private bank located in Dhaka, Bangladesh. The results of the study indicate that the Random Forest (RF) and Decision Tree (DT) models have achieved higher accuracy in predicting the outcomes when compared to other machine learning methods, with an accuracy of 92 and 94%, respectively. This study also highlights the importance of feature selection and prediction boosting in order to optimize the credit default prediction rates.

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References

  1. Ozili PK, Outa E (2017) Bank loan loss provisions research: a review. Borsa Istanbul Rev 17(3):144–163

    Article  Google Scholar 

  2. Malczyk A (2011) Good debt, bad debt: your money. Pers Financ 2011(367):9–10

    Google Scholar 

  3. Hanson J (2006) Good debt, bad debt: Knowing the difference can save your financial life. Jon Hanson

    Google Scholar 

  4. Löeffler G, Posch PN (2011) Credit risk modeling using Excel and VBA. Wiley

    Google Scholar 

  5. Ghorbani R, Kordestani G, Haghighat H, Ghaemi MH, Azizmohammadlou H (2021) Developing a model for evaluating the effectiveness of risk management in the banking industry. Financ Res J 22(4):496–520

    Google Scholar 

  6. Madaan M, Kumar A, Keshri C, Jain R, Nagrath P (2021) Loan default prediction using decision trees and random forest: a comparative study. In: IOP conference series: materials science and engineering, vol 1022, no 1, p 012042

    Google Scholar 

  7. Coşer A, Maer-matei MM, Albu C (2019) Predictive models for loan default risk assessment. Econ Comput Econ Cybern Stud Res 53(2)

    Google Scholar 

  8. Li Z, Li K, Yao X, Wen Q (2019) Predicting prepayment and default risks of unsecured consumer loans in online lending. Emerg Mark Financ Trade 55(1):118–132

    Article  Google Scholar 

  9. Anand M, Velu A, Whig P (2022) Prediction of loan behaviour with machine learning models for secure banking. J Comput Sci Eng (JCSE) 3(1):1–13

    Article  Google Scholar 

  10. Zanin L (2020) Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market. J Behav Exp Financ 25:100272

    Article  Google Scholar 

  11. Conklin JD (2002) Applied logistic regression

    Google Scholar 

  12. Steinbach M, Tan PN (2009) kNN: k-nearest neighbors. The top ten algorithms in data mining, pp 151–162

    Google Scholar 

  13. Ma Y, Guo G (2014) Support vector machines applications, vol 649. Springer

    Google Scholar 

  14. Leung KM et al (2007) Naive bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering, vol 2007, pp 123–156

    Google Scholar 

  15. Izza Y, Ignatiev A, Marques-Silva J (2020) On explaining decision trees. arXiv preprint arXiv:2010.11034

  16. Schonlau M, Zou RY (2020) The random forest algorithm for statistical learning. Stata J 20(1):3–29

    Article  Google Scholar 

Download references

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Correspondence to Mohammad Salah Uddin .

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Uddin, M.S., Rahman, M.A. (2024). A Comparative Study of Machine Learning Algorithms for Enhanced Credit Default Prediction. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_15

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