Journal Article

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

Giannozzi, Alessandro, Altman, Edward I, Marzi, Giacomo, Ciampi, Francesco

2021

Overview

Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.

• Publisher: Springer Nature B.V
• Subject: Accounting / Accuracy / Artificial intelligence / Banks / Bibliometrics / Citations / COVID-19 / Credit risk / Default / Economic crisis / Economic research / International finance / Knowledge management / Literary criticism / Literature reviews / Machine learning / Pandemics / Peer review / Prediction models / Predictions / Small & medium sized enterprises-SME / Small business / Statistical analysis / Statistics / Systematic review