Models for Predicting Business Bankruptcies and Their Application to Banking and Financial Regulation

18 Pages Posted: 14 Feb 2019 Last revised: 26 Feb 2021

See all articles by James Ming Chen

James Ming Chen

Michigan State University - College of Law

Date Written: February 5, 2019

Abstract

Models for predicting business bankruptcies have evolved rapidly. Machine learning is displacing traditional statistical methodologies. Three distinct techniques for approaching the classification problem in bankruptcy prediction have emerged: single classification, hybrid classifiers, and classifier ensembles. Methodological heterogeneity through the introduction and integration of machine-learning algorithms (especially support vector machines, decision trees, and genetic algorithms) has improved the accuracy of bankruptcy prediction models. Improved natural language processing has enabled machine learning to combine textual analysis of corporate filings with evaluation of numerical data. Greater accuracy promotes external processes of banks by minimizing credit risk and by facilitating regulatory compliance.

Keywords: bankruptcy prediction, classifier ensembles, hybrid classifiers, support vector machine, genetic algorithm, credit risk

Suggested Citation

Chen, James Ming, Models for Predicting Business Bankruptcies and Their Application to Banking and Financial Regulation (February 5, 2019). Penn State Law Review, vol. 123, pp. 735-752, 2019, Available at SSRN: https://ssrn.com/abstract=3329147 or http://dx.doi.org/10.2139/ssrn.3329147

James Ming Chen (Contact Author)

Michigan State University - College of Law ( email )

318 Law College Building
East Lansing, MI 48824-1300
United States

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