Predicting bankruptcy using machine learning algorithms release_tzmnrzz6grc3ngdpv7kpqzveda

Abstract

Bankruptcy prediction is of great utility for all economic stakeholders. Therefore, diverse methods have been applied for the early detection of financial risks in recent years. The objective of this paper is to propose an ensemble artificial intelligence (AI) model for effectively predicting the bankruptcy of a company. This study is designed to assess various classification algorithms over two bankruptcy datasets - Polish companies bankruptcy and Qualitative bankruptcy. The comparison results show that the bagging-ensemble model outperforms the others in predicting bankruptcy datasets. In particular, with the test data of Polish companies bankruptcy, the regression tree learner bagging (REPTree-bagging) ensemble model yields an accuracy of 100%. In predicting Qualitative bankruptcy dataset, the Random tree bagging (RTree-bagging) ensemble model has the highest accuracy with 96.2% compared to other models.
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