useful in both evaluating operational performance and financially valuing projects, by capturing interactions between key variables and quantifying the inherent variability of the business. This work follows this methodological tradition but focuses specifically on the financial risk of gold mining projects, where gold price volatility, metallurgical recovery, and operating costs jointly influence value creation. Conventional industry practice typically relies on univariate sensitivity analysis and deterministic scenarios (optimistic, base case, and pessimistic). While these tools are useful for a first approximation, they are insufficient to represent the stochastic and simultaneous nature of the main drivers of value. In gold projects, the correlation between variables, for example, grades, recoveries, costs, and their concurrent variability can significantly alter the Net Present Value (NPV) distribution compared to what a single reading would suggest. Therefore, a framework that integrates probability distributions and correlations is required to derive comparable operational and financial risk metrics. Conventional industry practice typically relies on univariate sensitivity analysis and deterministic scenarios (optimistic, base case, and pessimistic). While these tools are useful for a first approximation, they are insufficient to represent the stochastic and simultaneous nature of the main drivers of value. In gold projects, the correlation between variables for example, grades, recoveries, costs, and their concurrent variability can significantly alter the Net Present Value (NPV) distribution compared to what a single reading would suggest. Therefore, a framework that integrates probability distributions and correlations is required to derive comparable operational and financial risk metrics. Methodologically, the study fits each input variable with an appropriate empirical or theoretical distribution (e.g., Normal, Lognormal, or Triangular) using parameters obtained from historical series and internal projections and incorporates correlation structures consistent with the business logic. This probabilistic construction avoids deterministic extrapolations and improves risk traceability throughout the chain of assumptions, offering a comprehensive view of the outcome space and the residual uncertainty of the base case. The work makes a twofold contribution. First, it operationalizes financial risk assessment in gold mining projects using a reproducible framework that can be implemented in widely used tools, integrating random number generation, sensitivity analysis, and the reading of economic percentiles. Second, it shifts risk management from a qualitative exercise to a quantitative and predictive platform that strengthens investment decision-making, planning, and hedging, aligned with long-term financial sustainability criteria in the gold industry. Taken together, the proposed approach allows management to accurately estimate the probability of achieving NPV/IRR targets and prioritize value drivers under uncertainty.
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