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Ensemble Learning or Deep Learning? Application to Default Risk Analysis

Author

Listed:
  • 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)

Abstract

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
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    References listed on IDEAS

    as
    1. 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.
    2. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
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    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," JRFM, MDPI, vol. 13(3), pages 1-16, March.
    3. Ryno du Plooy & Pierre J. Venter, 2021. "A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework," JRFM, MDPI, vol. 14(6), pages 1-18, June.
    4. Cheng-Chien Lai & Wei-Hsin Huang & Betty Chia-Chen Chang & Lee-Ching Hwang, 2021. "Development of Machine Learning Models for Prediction of Smoking Cessation Outcome," IJERPH, MDPI, vol. 18(5), pages 1-10, March.
    5. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    6. Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
    7. 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.
    8. Shigeyuki Hamori, 2020. "Empirical Finance," JRFM, MDPI, vol. 13(1), pages 1-3, January.
    9. Marc Andreas Schmitt, 2022. "Deep Learning in Business Analytics: A Clash of Expectations and Reality," Papers 2205.09337, arXiv.org.
    10. Marc Schmitt, 2022. "Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring," Papers 2205.10535, arXiv.org.
    11. 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.
    12. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    13. Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," JRFM, MDPI, vol. 11(4), pages 1-13, October.
    14. 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.
    15. Thackway, William & Ng, Matthew Kok Ming & Lee, Chyi Lin & Pettit, Christopher, 2021. "Building a predictive machine learning model of gentrification in Sydney," SocArXiv hkc96, Center for Open Science.

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    More about this item

    Keywords

    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

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