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Data mining feature selection for credit scoring models

Author

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  • Y Liu

    (University of Goettingen)

  • M Schumann

    (University of Goettingen)

Abstract

The features used may have an important effect on the performance of credit scoring models. The process of choosing the best set of features for credit scoring models is usually unsystematic and dominated by somewhat arbitrary trial. This paper presents an empirical study of four machine learning feature selection methods. These methods provide an automatic data mining technique for reducing the feature space. The study illustrates how four feature selection methods—‘ReliefF’, ‘Correlation-based’, ‘Consistency-based’ and ‘Wrapper’ algorithms help to improve three aspects of the performance of scoring models: model simplicity, model speed and model accuracy. The experiments are conducted on real data sets using four classification algorithms—‘model tree (M5)’, ‘neural network (multi-layer perceptron with back-propagation)’, ‘logistic regression’, and ‘k-nearest-neighbours’.

Suggested Citation

  • Y Liu & M Schumann, 2005. "Data mining feature selection for credit scoring models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1099-1108, September.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:9:d:10.1057_palgrave.jors.2601976
    DOI: 10.1057/palgrave.jors.2601976
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    References listed on IDEAS

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    1. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
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    Cited by:

    1. Juan Laborda & Seyong Ryoo, 2021. "Feature Selection in a Credit Scoring Model," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    2. Hariharan, Naveen Kunnathuvalappil, 2019. "Maintaining Financial Data Quality For Business Intelligence," OSF Preprints w7n26, Center for Open Science.
    3. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
    4. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    5. Weidong Guo & Zach Zhizhong Zhou, 2022. "A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1248-1313, September.
    6. Pérez-Martín, A. & Pérez-Torregrosa, A. & Vaca, M., 2018. "Big Data techniques to measure credit banking risk in home equity loans," Journal of Business Research, Elsevier, vol. 89(C), pages 448-454.
    7. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    8. Agustin Pérez-Martín & Agustin Pérez-Torregrosa & Alejandro Rabasa & Marta Vaca, 2020. "Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    9. Njeru, Andrew Kioi, 2022. "Asset quality assessment in the absence of quality data towards optimal credit intermediation," KBA Centre for Research on Financial Markets and Policy Working Paper Series 63, Kenya Bankers Association (KBA).
    10. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    11. K Falangis & J J Glen, 2010. "Heuristics for feature selection in mathematical programming discriminant analysis models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(5), pages 804-812, May.
    12. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.

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