Corporate Distress- One of the Major Hindrances to Sustainable Development of Africa

Louisa Muparuri and Victor Gumbo ()
Additional contact information
Victor Gumbo: University of Botswana

Africagrowth Agenda, 2022, vol. 19, issue 1, 10-13

Abstract: Corporate financial distress prediction is a pivotal aspect of economic development. The ability to foretell that a company will be getting into financial distress is essential for decision-makers, shareholders, and policymakers in making the best decisions and policies for sustainable development. Prediction accuracy is paramount in the implementation of distress mitigation measures, a critical component to attract investment in particular to most of the developing countries in Africa. The advent of the fourth industrial revolution saw Artificial Intelligence (AI) taking centre stage in financial risk modelling. This growth has however not precluded the role of traditional statistical methods in modelling financial risk. There is a lack of consensus amongst academia and practitioners on the accuracy of these two groups of methodologies in distress prediction. Protagonists of the conventional school of thought still hold on to statistical methods being more accurate whilst the new age proponents believe AI has brought in higher levels of predictive strength and model accuracy. This study seeks to explore the role of Artificial Intelligence in corporate distress prediction and how Africa could benefit from incorporating such techniques in distress prediction. The output of this study will invariably enhance predictive modelling in Africa by promoting the use of AI techniques. Heightened prediction accuracy that comes with use of Artificial Intelligence techniques is bound to improve the return to shareholders by enhancing financial risk management within emerging markets. Artificial Intelligence, Artificial Neural Network, financial distress, prediction accuracy.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.co.za/doi/abs/10.10520/ejc-afgrow_v19_n1_a2 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:afj:journ2:v:19:y:2022:i:1:p:10-13

Access Statistics for this article

More articles in Africagrowth Agenda from Africagrowth Institute Contact information at EDIRC.
Bibliographic data for series maintained by Kirk De Doncker ().