An overview of bankruptcy prediction models for corporate firms: A Systematic literature review

Yin Shi, Xiaoni Li


Purpose: This paper aims to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between different authors (co-authorship), and to identify the primary models and methods that are used and studied by authors of this area in the past five decades.

Design/methodology/approach: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017.

Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, demonstrating the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as the researchers with a lot of influence were basically not working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence.

Originality/value: We applied the SLR approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this contributes as the link among different elements of the concept studied, and it demonstrates the growing interest in this area.


bankruptcy prediction, business failure, financial distress, insolvency, default firm, SLR

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This work is licensed under a Creative Commons Attribution 4.0 International License

Intangible Capital, 2004-2024

Online ISSN: 1697-9818; Print ISSN: 2014-3214; DL: B-33375-2004

Publisher: OmniaScience