It is difficult to imagine a business in the modern world of digitization that does not use technology and artificial intelligence in some capacity. Business transformation brought a severe time of trouble for small and medium-sized enterprises all over the world. Throughout our study, we have based analysis on factual information that highlights the critical function that the Information Technology (IT) industry plays in maintaining corporate relevance and encouraging customer involvement. Our investigation goes beyond merely highlighting the value of using big data to analyze financial crises in a predictive manner. It also emphasizes the proactive incorporation of artificial intelligence (AI) into corporate operations as a preventive step to avoid them. Our study includes the examination of viewpoints from people who were actively involved in their careers before lockdown procedures were implemented and incorporated into our research the opinions of people who work for small and medium-sized businesses and government agencies. Based on the literature that already exists on the critical role that Artificial Intelligence (AI) plays in protecting companies from disasters, we looked closely at a particular case study. The case study's conclusions highlight how important corporate automation is. In this article, we provide case studies of wellknown, globally renowned companies that demonstrate how they skillfully employ new technology to maintain their competitive position. These model organizations include both internationally recognized companies like Google and Facebook and local organizations like Sberbank. In conclusion, our research adopted an artificial intelligence framework that can help business organizations to predict problems and financial crises.
CITATION STYLE
Hamada, M. A., & Alhyasat, K. M. K. (2023). Artificial Intelligence Technology to Predict the Financial Crisis in Business Companies. In Eurasia Proceedings of Science, Technology, Engineering and Mathematics (Vol. 24, pp. 71–82). ISRES Publishing. https://doi.org/10.55549/epstem.1406233
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