Figure 1 – Financial risk analysis flow with Monte Carlo Simulation. SCOPE This work falls within the thematic area of AI and data-driven decision-making, contributing a quantitative and predictive approach to the financial evaluation of gold mining projects. The paper's scope encompasses the development, implementation, and validation of a Monte Carlo simulation model that transforms historical data and operational parameters into probabilistic metrics of value, thereby improving the quality and robustness of investment decisions under conditions of high uncertainty. The study focuses on modeling key variables in the mining business gold price, metallurgical recovery, and mining and processing costs from which empirically or theoretically adjusted probability distributions are derived. These distributions, combined with correlation structures representative of operational reality, feed into an iterative simulation model that generates a Net Present Value (NPV) distribution. This allows the analysis to move away from traditional deterministic scenarios and adopt a data-driven approach that reflects the stochastic nature of the business. The scope also includes the development of advanced risk indicators, such as the project's Value at Risk (VaR) and the probability of reaching profitability thresholds. Furthermore, it incorporates a variance contribution analysis, which identifies and quantifies the relative weight of each variable in the overall financial risk. This disaggregation allows for prioritizing control, optimization, and monitoring efforts, integrating the model within a data-driven decision governance framework that supports the corporate strategy. From the Congress's perspective, the paper demonstrates how advanced analytics and probabilistic modeling techniques, typically associated with data science, can be integrated into the mining project evaluation process. This transforms simulation into a tool that not only estimates
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