IDEAS home Printed from
   My bibliography  Save this paper

Ensemble Learning or Deep Learning? Application to Default Risk Analysis


  • Shigeyuki Hamori

    (Graduate School of Economics, Kobe University)

  • Minami Kawai

    (Department of Economics, Kobe University)

  • Takahiro Kume

    (Department of Economics, Kobe University)

  • Yuji Murakami

    (Department of Economics, Kobe University)

  • Chikara Watanabe

    (Department of Economics, Kobe University)


Proper credit risk management is essential for lending institutions as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasing important. This study analyzed default payment data from Taiwan and compared the prediction accuracy and classification ability of three ensemble learning methods-specifically, Bagging, Random Forest, and Boosting-with those of various neural network methods, each of which has a different activation function. The results indicate that Boosting has a high prediction accuracy, whereas that of Bagging and Random Forest is relatively low. They also indicate that the prediction accuracy and classification performance of Boosting is better than that of deep neural networks, Bagging, and Random Forest.

Suggested Citation

  • Shigeyuki Hamori & Minami Kawai & Takahiro Kume & Yuji Murakami & Chikara Watanabe, 2018. "Ensemble Learning or Deep Learning? Application to Default Risk Analysis," Discussion Papers 1802, Graduate School of Economics, Kobe University.
  • Handle: RePEc:koe:wpaper:1802

    Download full text from publisher

    File URL:
    Download Restriction: no

    Other versions of this item:


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. repec:agr:journl:v:4(621):y:2019:i:4(621):p:75-84 is not listed on IDEAS
    2. Yuchen Zhang & Shigeyuki Hamori, 2020. "The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(3), pages 1-16, March.
    3. Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.
    4. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, Open Access Journal, vol. 7(1), pages 1-22, March.
    5. Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," International Association of Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
    6. 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 - AGER, vol. 0(4(621), W), pages 75-84, Winter.
    7. Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(4), pages 1-13, October.
    8. Nikolaos Sariannidis & Stelios Papadakis & Alexandros Garefalakis & Christos Lemonakis & Tsioptsia Kyriaki-Argyro, 2020. "Default avoidance on credit card portfolios using accounting, demographical and exploratory factors: decision making based on machine learning (ML) techniques," Annals of Operations Research, Springer, vol. 294(1), pages 715-739, November.
    9. Shigeyuki Hamori, 2020. "Empirical Finance," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(1), pages 1-3, January.

    More about this item


    credit risk; ensemble learning; deep learning; bagging; random forest; boosting; deep neural network.;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • E - Macroeconomics and Monetary Economics
    • F2 - International Economics - - International Factor Movements and International Business
    • F3 - International Economics - - International Finance
    • G - Financial Economics

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:koe:wpaper:1802. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kimiaki Shirahama The email address of this maintainer does not seem to be valid anymore. Please ask Kimiaki Shirahama to update the entry or send us the correct address (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.