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A Deep Neural Network (DNN) based classification model in application to loan default prediction

Author

Listed:
  • Selçuk BAYRACI

    (R&D Center, C/S Information Technologies, Istanbul, Turkey)

  • Orkun SUSUZ

    (R&D Center, C/S Information Technologies, Istanbul, Turkey)

Abstract

In this study, we applied a Deep Neural Networks (DNN) based classification model along with the conventional classification methods (Logistic Regression, Decision Tree, Naïve Bayes and Support Vector Machines) on a two distinct datasets containing characteristics of the loan clients in a medium-sized Turkish commercial bank. Python programming language and libraries (Sklearn, Tensorflow and Keras) have been used in data cleaning, data preparation, feature engineering and model implementation processes. Our empirical findings document that the accuracy of the deep learning classification model increases with the size of the dataset, implying that the deep learning models might yield better results than regression-based models in more complex datasets.

Suggested Citation

  • Selçuk BAYRACI & Orkun SUSUZ, 2019. "A Deep Neural Network (DNN) based classification model in application to loan default prediction," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 75-84, Winter.
    • Handle: RePEc:agr:journl:v:xxvi:y:2019:i:4(621):p:75-84
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    References listed on IDEAS

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    1. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    3. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    4. 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.
    5. Chen, Shiyi & Härdle, Wolfgang Karl & Moro, Rouslan A., 2006. "Estimation of default probabilities with Support Vector Machines," SFB 649 Discussion Papers 2006-077, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    Cited by:

    1. Sabek Amine, 2023. "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 9(1), pages 16-32, July.
    2. Caplescu Raluca Dana & Panaite Ana-Maria & Pele Daniel Traian & Strat Vasile Alecsandru, 2020. "Will they repay their debt? Identification of borrowers likely to be charged off," Management & Marketing, Sciendo, vol. 15(3), pages 393-409, September.
    3. Vikram Ojha & JeongHoe Lee, 2021. "Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009," Digital Finance, Springer, vol. 3(3), pages 249-271, December.

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