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Bayesian regularized artificial neural networks for the estimation of the probability of default

Author

Listed:
  • Eduard Sariev
  • Guido Germano

Abstract

Artificial neural networks (ANNs) have been extensively used for classification problems in many areas such as gene, text and image recognition. Although ANNs are popular also to estimate the probability of default in credit risk, they have drawbacks; a major one is their tendency to overfit the data. Here we propose an improved Bayesian regularization approach to train ANNs and compare it to the classical regularization that relies on the back-propagation algorithm for training feed-forward networks. We investigate different network architectures and test the classification accuracy on three data sets. Profitability, leverage and liquidity emerge as important financial default driver categories.

Suggested Citation

  • Eduard Sariev & Guido Germano, 2020. "Bayesian regularized artificial neural networks for the estimation of the probability of default," Quantitative Finance, Taylor & Francis Journals, vol. 20(2), pages 311-328, February.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:2:p:311-328
    DOI: 10.1080/14697688.2019.1633014
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    1. Caplescu Raluca Dana & Cojocea Manuela-Simona & Pele Daniel Traian & Strat Vasile Alecsandru, 2021. "Improvements in PD models. A case-study approach," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 15(1), pages 13-32, December.
    2. Huang, Hsiao-Tzu & Hwang, Yawen & Chan, Linus Fang-Shu & Tsai, Chenghsien Jason, 2024. "Value-enhancing modeling of surrenders and lapses," Insurance: Mathematics and Economics, Elsevier, vol. 119(C), pages 48-63.
    3. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
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    5. Farwah Ali Syed & Kwo-Ting Fang & Adiqa Kausar Kiani & Muhammad Shoaib & Muhammad Asif Zahoor Raja, 2025. "Design of Neuro-Stochastic Bayesian Networks for Nonlinear Chaotic Differential Systems in Financial Mathematics," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 241-270, January.
    6. Calabrese, G.G. & Falavigna, G. & Ippoliti, R., 2024. "Financial constraints prediction to lead socio-economic development: An application of neural networks to the Italian market," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    7. 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.
    8. Timothy Praditia & Thilo Walser & Sergey Oladyshkin & Wolfgang Nowak, 2020. "Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture," Energies, MDPI, vol. 13(15), pages 1-26, July.
    9. Wei Li & Florentina Paraschiv & Georgios Sermpinis, 2022. "A data-driven explainable case-based reasoning approach for financial risk detection," Quantitative Finance, Taylor & Francis Journals, vol. 22(12), pages 2257-2274, December.
    10. Jaewon Park & Minsoo Shin & Wookjae Heo, 2021. "Estimating the BIS Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting by Variable Selection Using Random Forest Algorithms," Risks, MDPI, vol. 9(2), pages 1-19, February.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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