Intelligent Risk Analysis of Investment Projects in the Extractive Industry

Abstract

This study introduces an advanced technology for risk analysis in investment projects within the extractive industry, specifically focusing on innovative mining ventures.The research primarily investigates various determinants influencing project risks, including production efficiency, cost, informational content, resource potential, organizational structure, external environmental influences, and environmental impacts.In addressing the research challenge, system-cognitive models from the Eidos intellectual framework are employed.These models quantitatively reflect the informational content observed across different gradations of descriptive scales, predicting the transition of the modelled object into a state corresponding to specific class gradations.A comprehensive analysis of strengths, weaknesses, opportunities and threats (SWOT) has been conducted, unveiling the dynamic interplay of development factors against the backdrop of threats and opportunities within mineral deposits exploitation projects.This analysis facilitates the identification of critical problem areas, bottlenecks, prospects, and risks, considering environmental considerations.The application of this novel intelligent technology significantly streamlines the development process for mining investment projects, guiding the selection of ventures that promise enhanced production efficiency, cost reduction, and minimized environmental harm.The methodological approach adopted in this study aligns with the highest standards of academic rigour, ensuring consistency in the use of professional terminology throughout the article and adhering to the stylistic and structural norms prevalent in leading academic journals.By leveraging an intelligent, systematic framework for risk analysis, this research contributes valuable insights into optimizing investment decisions in the mining sector, emphasizing sustainability and economic viability.

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