<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Personal bankruptcy prediction using machine learning techniques

Economics and Business Review

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Personal bankruptcy prediction using machine learning techniques"}

It has become crucial to have an early prediction model that provides accurate assurance for users about the financial situation of consumers. Recent studies...

Personal bankruptcy prediction using machine learning techniques

Data publikacji: 13 lip 2024

Zakres stron: 118 - 142

Otrzymano: 23 sty 2024

Przyjęty: 07 kwi 2024

DOI: https://doi.org/10.18559/ebr.2024.2.1149

Słowa kluczowe
personal bankruptcy, SVM, random forest, AdaBoost, XGBoost, LightGBM, CatBoost, SHAP

© 2024 Magdalena Brygała et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Personal bankruptcy prediction using machine learning techniques

Data publikacji: 13 lip 2024

Zakres stron: 118 - 142

Otrzymano: 23 sty 2024

Przyjęty: 07 kwi 2024

DOI: https://doi.org/10.18559/ebr.2024.2.1149

Słowa kluczowepersonal bankruptcy, SVM, random forest, AdaBoost, XGBoost, LightGBM, CatBoost, SHAP

© 2024 Magdalena Brygała et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
personal bankruptcy, SVM, random forest, AdaBoost, XGBoost, LightGBM, CatBoost, SHAP