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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
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    References listed on IDEAS

    as
    1. Liming Brotcke, 2022. "Time to Assess Bias in Machine Learning Models for Credit Decisions," JRFM, MDPI, vol. 15(4), pages 1-10, April.
    2. 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.
    Full references (including those not matched with items on IDEAS)

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    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

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