Volume 20 , Issue 4 , PP: 152-163, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
J. Ramón R. de Vega 1 * , A. G. Ruiz Conejo 2 , Carlos C. Carranza 3 , Vladimir R. Cairo 4
Doi: https://doi.org/10.54216/IJNS.200412
Nowadays, Machine Learning techniques stand out, especially in the business sector, in predicting bankruptcies in small and medium-sized enterprises (SMEs). This reduces the probability of making bad investments when creating SMEs. Therefore, a systematic review of Machine Learning for predicting bankruptcies in SMEs was conducted to identify ideal articles. The search was conducted on Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, ACM Digital Library, Google Scholar, and ProQuest. As a result, information was collected from 84 definitive studies on determining bankruptcies in SMEs using Machine Learning. Therefore, this study aims to determine the state-of-the-art regarding determining bankruptcies in SMEs using Machine Learning. To obtain the results, the Saaty Neutrosophic AHP method was used to identify the most applied business sector and predict possible bankruptcy due to its broad nature of indeterminacy in that subset. The systematic review results have allowed for determining essential details regarding the state-of-the-art of determining bankruptcies in SMEs using Machine Learning.
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