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Rethinking SME default prediction: a systematic literature review and future perspectives.

Francesco Ciampi1, Alessandro Giannozzi1, Giacomo Marzi2

  • 1University of Florence, Via delle Pandette, 9, 50127 Florence, IT Italy.

Scientometrics
|February 3, 2021
PubMed
Summary

This study reviews 100+ articles on small and medium enterprise (SME) default prediction, identifying key research streams. It proposes future directions using AI and machine learning for improved SME financial risk assessment.

Keywords:
BankruptcyBibliometric analysisCredit riskCredit scoringDefault predictionFailureRatingRisk predictionSME survivalSMEsSystematic literature reviewVOSviewer

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Area of Science:

  • Finance, Management, Accounting, Statistics
  • Financial Risk Management
  • Business Analytics

Background:

  • Small and medium enterprises (SMEs) are vital to economies but vulnerable to financial crises.
  • The 2007-2009 global financial crisis and COVID-19 highlighted the need for robust SME default prediction models.
  • Existing SME default prediction research spans multiple disciplines.

Purpose of the Study:

  • To systematically review and analyze the literature on SME default prediction modelling.
  • To identify distinct research streams within the SME default prediction domain.
  • To propose future research directions for enhanced predictive accuracy.

Main Methods:

  • Systematic literature review of over 100 peer-reviewed articles.
  • Statistical and bibliometric analysis of publications from 1986 to 2019.
  • Identification and analysis of five major research streams.

Main Results:

  • The SME default prediction field has grown significantly over 34 years.
  • Five distinct streams of research in SME default prediction were identified and analyzed.
  • The review provides a foundation for understanding the evolution of SME default prediction.

Conclusions:

  • Future research should leverage artificial intelligence (AI) and machine learning (ML) for SME default prediction.
  • Incorporating macro-data inputs and novel data sources can enhance predictive models.
  • Developing advanced analytical techniques is crucial for addressing emerging economic challenges in SME finance.