Investment risk analysis in emerging markets using artificial intelligence techniques
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Abstract
This study aims to analyze investment the risks in emerging markets using artificial intelligence techniques, focusing on the Egyptian case as an applied model, as this study is based on the fact that emerging markets, despite the high-return investment opportunities, but they are characterized by a great degree of uncertainty and multiple risks, including political, economic, legal, and regulatory fluctuations. This is where the need for advanced analytical tools beyond traditional models emerged, and artificial intelligence was the perfect choice for this challenge.
The study also relied on a descriptive analytical approach, and applied statistical tools and machine learning techniques such as logistic regression, decision tree, and clustering algorithms (K-Means) to analyze market data, in integration with Python and excel tools. The results showed a statistically significant strong correlation between the use of AI technologies and the accuracy of risk analysis, correlation coefficients exceeded 0.8 in most models. The results also showed that the success of artificial intelligence depends on four basic dimensions: physical capabilities (infrastructure), organizational capabilities (policies and regulations), human competencies, and technical systems.
The study recommends the development of national strategies to localize artificial intelligence technologies, provide incentives for startups in this field, while strengthening the digital infrastructure and implementing sustainable training programs to qualify competencies. It also emphasizes the importance of public-private and academic partnerships to support innovation and achieve sustainable economic growth in emerging markets. This study reflects a qualitative contribution to linking artificial intelligence with the analysis of investment decisions in high-risk environments, and opens the way for deeper future research using more advanced artificial intelligence models.
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