Article: Bankruptcy risk prediction models: a systematic literature review and bibliometric analysis Journal: International Journal of Business Continuity and Risk Management (IJBCRM) 2026 Vol.16 No.3 pp.251 - 275 Abstract: Predicting bankruptcy risk is crucial for a variety of stakeholders, including investors, creditors, financial experts, and corporate executives. By identifying businesses that could be financially unstable, the ability to effectively estimate bankruptcy risk helps investors make well-informed decisions and perhaps avoid suffering large losses. Therefore, it is important to investigate the research trend in bankruptcy risk prediction models. Using a comprehensive search of the Scopus database, this study analysed 402 research papers published between 1994 and 2025 in seven prediction models including the Altman Z score, the Ohlson, the Springate, the Zmijewski, the Taffler, the Grover and the Feltham model. We use a range of approaches, such as publication trends, the citation network, methodological analysis, content analysis, and keyword analysis, to identify significant work in the bankruptcy risk prediction area. Bibliometric analysis was performed using R software. In addition to identifying relevant areas that need more investigation, this study will help managers, creditors, regulators, investors, policymakers, and academic researchers comprehend various risk prediction models to monitor the financial health of the organisation and identify relevant areas that need more investigation. Inderscience Publishers - linking academia, business and industry through research

Title: Bankruptcy risk prediction models: a systematic literature review and bibliometric analysis

Authors: Payal Kedia; Lokanath Mishra

Addresses: JK Lakshmipat University, Near Mahindra SEZ, Mahapura, Ajmer Road, Jaipur – 302026, Rajasthan, India ' JK Lakshmipat University, Near Mahindra SEZ, Mahapura, Ajmer Road, Jaipur – 302026, Rajasthan, India

Abstract: Predicting bankruptcy risk is crucial for a variety of stakeholders, including investors, creditors, financial experts, and corporate executives. By identifying businesses that could be financially unstable, the ability to effectively estimate bankruptcy risk helps investors make well-informed decisions and perhaps avoid suffering large losses. Therefore, it is important to investigate the research trend in bankruptcy risk prediction models. Using a comprehensive search of the Scopus database, this study analysed 402 research papers published between 1994 and 2025 in seven prediction models including the Altman Z score, the Ohlson, the Springate, the Zmijewski, the Taffler, the Grover and the Feltham model. We use a range of approaches, such as publication trends, the citation network, methodological analysis, content analysis, and keyword analysis, to identify significant work in the bankruptcy risk prediction area. Bibliometric analysis was performed using R software. In addition to identifying relevant areas that need more investigation, this study will help managers, creditors, regulators, investors, policymakers, and academic researchers comprehend various risk prediction models to monitor the financial health of the organisation and identify relevant areas that need more investigation.

Keywords: bibliometric analysis; systematic literature review; SLR; content analysis; bankruptcy; risk models.

DOI: 10.1504/IJBCRM.2026.155767

International Journal of Business Continuity and Risk Management, 2026 Vol.16 No.3, pp.251 - 275

Received: 11 Apr 2025
Accepted: 10 Mar 2026
Published online: 13 Aug 2026 *

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Title: Bankruptcy risk prediction models: a systematic literature review and bibliometric analysis

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