A Systematic Literature Review on Corporate Insolvency Prevention Using Artificial Intelligence Algorithms

Abstract

The research field of insolvency avoidance was particularly fuelled by the economic crisis of 2008. Corporate insolvencies cause financial as well as non-financial damage to the entrepreneurs, to creditors, but also to society through lost jobs or taxes. Neural networks, but also regressions and decision trees, represent interesting approaches to insolvency prevention due to increasing computer power and data availability. This paper presents the current state of research on these selected algorithms in insolvency avoidance. The paper therefore provides a contribution to structuring the current state of research and shows the trends of the research field. For this purpose, a systematic literature search was carried out in the scientific databases with reference to the field of business administration and management with focus on an engineering environment using defined keywords, and the results were processed and analysed. One result is that, due to the different data availability and parameters, research is being carried out into different algorithms for avoiding insolvency and the trend is towards insolvency prevention for SMEs.