IDEAS home Printed from https://ideas.repec.org/a/vrs/ecobur/v10y2024i2p118-142n1004.html
   My bibliography  Save this article

Personal bankruptcy prediction using machine learning techniques

Author

Listed:
  • Brygała Magdalena

    (Faculty of Management and Economics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland)

  • Korol Tomasz

    (Faculty of Management and Economics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland)

Abstract

It has become crucial to have an early prediction model that provides accurate assurance for users about the financial situation of consumers. Recent studies have focused on predicting corporate bankruptcies and credit defaults, not personal bankruptcies. Due to this situation, the present study fills the literature gap by comparing different machine learning algorithms to predict personal bankruptcy. The main objective of the study is to examine the usefulness of machine learning models such as SVM, random forest, AdaBoost, XGBoost, LightGBM, and CatBoost in forecasting personal bankruptcy. The study relies on two samples of households (learning and testing) from the Survey of Consumer Finances, which was conducted in the United States. Among the models estimated, LightGBM, CatBoost, and XGBoost showed the highest effectiveness. The most important variables used in the models are income, refusal to grant credit, delays in the repayment of liabilities, the revolving debt ratio, and the housing debt ratio.

Suggested Citation

  • Brygała Magdalena & Korol Tomasz, 2024. "Personal bankruptcy prediction using machine learning techniques," Economics and Business Review, Sciendo, vol. 10(2), pages 118-142.
    • Handle: RePEc:vrs:ecobur:v:10:y:2024:i:2:p:118-142:n:1004
    DOI: 10.18559/ebr.2024.2.1149
    as

    Download full text from publisher

    File URL: https://doi.org/10.18559/ebr.2024.2.1149
    Download Restriction: no
    File URL: https://libkey.io/10.18559/ebr.2024.2.1149?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xin Wang & Kai Zong & Cuicui Luo, 2022. "Credit risk detection based on machine learning algorithms," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 11(3), pages 183-189.
    2. Liming Brotcke, 2022. "Time to Assess Bias in Machine Learning Models for Credit Decisions," JRFM, MDPI, vol. 15(4), pages 1-10, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nartey Menzo, Benjamin Prince & Mogre, Diana & Asuamah Yeboah, Samuel, 2024. "Beyond Income: The Complexities of Credit Risk in Developing Countries," MPRA Paper 122364, University Library of Munich, Germany, revised 20 Sep 2024.
    2. Alex Chernoff & Gabriela Galassi, 2023. "Digitalization: Labour Markets," Discussion Papers 2023-16, Bank of Canada.
    3. Rogojan Luana Cristina & Croicu Andreea Elena & Iancu Laura Andreea, 2023. "Modern Approaches in Credit Risk Modeling: A Literature Review," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1617-1627, July.
    4. Tanja Verster & Erika Fourie, 2023. "The Changing Landscape of Financial Credit Risk Models," IJFS, MDPI, vol. 11(3), pages 1-15, August.
    5. Mark Potanin & Andrey Chertok & Konstantin Zorin & Cyril Shtabtsovsky, 2023. "Startup success prediction and VC portfolio simulation using CrunchBase data," Papers 2309.15552, arXiv.org.
    6. Xue Wen Tan & Stanley Kok, 2024. "Explainable Risk Classification in Financial Reports," Papers 2405.01881, arXiv.org, revised Dec 2024.
    7. Sahab Zandi & Kamesh Korangi & Mar'ia 'Oskarsd'ottir & Christophe Mues & Cristi'an Bravo, 2024. "Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction," Papers 2402.00299, arXiv.org, revised Jun 2024.
    8. Anton van Dyk & Gary van Vuuren, 2023. "Measurement and Calibration of Regulatory Credit Risk Asset Correlations," JRFM, MDPI, vol. 16(9), pages 1-19, September.
    9. Pei, Youquan & Peng, Heng & Xu, Jinfeng, 2024. "A latent class Cox model for heterogeneous time-to-event data," Journal of Econometrics, Elsevier, vol. 239(2).
    10. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    11. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.

    More about this item

    Keywords

    personal bankruptcy; SVM; random forest; AdaBoost; XGBoost; LightGBM; CatBoost ; SHAP;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

    Statistics

    Access and download statistics

    Corrections

    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:vrs:ecobur:v:10:y:2024:i:2:p:118-142:n:1004. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.