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Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Network Approach

Author

Listed:
  • Sihem Khemakhem
  • Younes Boujelbene

    (University of Sfax, Tunisia)

Abstract

Banks are concerned with the assessment of the risk of financial distress before giving out a loan. Many researchers proposed the use of models based on the Neural Networks in order to help the banker better make a decision. The objective of this paper is to explore a new practical way based on the Neural Networks that would help the banker to predict the non payment risk the companies asking for a loan. This work is motivated by the insufficiency of traditional prevision models. The sample consists of 86 Tunisian companies and 15 financial ratios were calculated, over the period from 2005 to 2007. The results were compared with those of discriminant analysis. They show that the neural networks technique is more accurate in term of predictability.

Suggested Citation

  • Sihem Khemakhem & Younes Boujelbene, 2015. "Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Network Approach," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 14(1), pages 60-78, March.
    • Handle: RePEc:ami:journl:v:14:y:2015:i:1:p:60-78
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    Citations

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    Cited by:

    1. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    2. Shigeyuki Hamori & Minami Kawai & Takahiro Kume & Yuji Murakami & Chikara Watanabe, 2018. "Ensemble Learning or Deep Learning? Application to Default Risk Analysis," JRFM, MDPI, vol. 11(1), pages 1-14, March.
    3. Hitoshi Hamori & Shigeyuki Hamori, 2020. "Does Ensemble Learning Always Lead to Better Forecasts?," Applied Economics and Finance, Redfame publishing, vol. 7(2), pages 51-56, March.
    4. Aleksandra Wójcicka, 2017. "Neural Networks in Credit Risk Classification of Companies in the Construction Sector," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(2), pages 63-77, December.
    5. Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
    6. Alexey Litvinenko, 2023. "A Comparative Analysis of Altman's Z-Score and T. Jury's Cash-Based Credit Risk Models with The Application to The Production Company and The Data for The Years 2016-2022," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 22(3), pages 518-553, September.
    7. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    8. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. Piasecki Krzysztof & Wójcicka-Wójtowicz Aleksandra, 2017. "Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 129-143, December.
    10. T. Nguyen D. & T. Do T. & B. Nguyen N. & Т. Нгуен Д. & Т. До Т. & Б. Нгуен Н., 2016. "Применение дискриминационной модели в управлении риском потребительских кредитов в коммерческом банке Вьетнама // Applying Discriminant Model to Manage Credit Risk for Consumer Loans in Vietnamese Com," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 4(4), pages 5-16.

    More about this item

    Keywords

    credit risk; prediction; discriminant analysis; artificial neural networks;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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