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Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression

Author

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  • Zaghdoudi Khemais
  • Djebali Nesrine
  • Mezni Mohamed

Abstract

This paper aims to develop models for foreseeing default risk of small and medium enterprises (SMEs) for one Tunisian commercial bank using two different methodologies (logistic regression and discriminant analysis). We used a database that consists of 195 credit files granted to Tunisian SMEs which are divided into five sectors ¡°industry, agriculture, tourism, trade and services¡± for a period from 2012 to 2014. The empirical results that we found support the idea that these two scoring techniques have a statistically significant power in predicting default risk of enterprises. Logistic discrimination classifies enterprises correctly in their original groups with a rate of 76.7% against 76.4% in case of linear discrimination giving so a slight superiority to the first method.

Suggested Citation

  • Zaghdoudi Khemais & Djebali Nesrine & Mezni Mohamed, 2016. "Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(4), pages 39-53, April.
    • Handle: RePEc:ibn:ijefaa:v:8:y:2016:i:4:p:39-53
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    References listed on IDEAS

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    Cited by:

    1. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    2. Alberto Manelli & Roberta Pace & Maria Leone, 2022. "Leverage, Growth Opportunities, and Credit Risk: Evidence from Italian Innovative SMEs," Risks, MDPI, vol. 10(4), pages 1-10, April.
    3. Daisy Delsile Dlamini & Jethro Zuwarimwe & Joseph Francis & Godwin R. A. Mchau, 2022. "Risk Factor Assessment of the Smallholder Baby Vegetable Production in Eswatini," Agriculture, MDPI, vol. 12(5), pages 1-11, April.
    4. Nigar Karimova, 2024. "Application of AI in Credit Risk Scoring for Small Business Loans: A case study on how AI-based random forest model improves a Delphi model outcome in the case of Azerbaijani SMEs," Papers 2410.05330, arXiv.org.

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    More about this item

    Keywords

    credit scoring; probability of default; discriminant analysis; logistic regression; SMEs;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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