Personal bankruptcy prediction using machine learning techniques

Magdalena Brygała; Tomasz Korol

doi:10.18559/ebr.2024.2.1149

Personal bankruptcy prediction using machine learning techniques

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.

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DOI:: Digital Object Identifier (open in new tab) 10.18559/ebr.2024.2.1149
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Articles

Type:

artykuły w czasopismach

Published in:

Economics and Business Review no. 10, pages 118 - 142,
ISSN: 2392-1641

Language:

English

Publication year:

2024

Bibliographic description:

Brygała M., Korol T.: Personal bankruptcy prediction using machine learning techniques// Economics and Business Review -Vol. 10,iss. 2 (2024), s.118-142

DOI: