## 1 Introduction
However, NPV estimation often overlooks natural variations in key parameters. [PERSON] et al. [28] proposed a methodology for quantifying risk in NPV estimates using Monte Carlo simulations, enhancing the understanding of how uncertainty impacts profitability. Monte Carlo simulations generate distributions of key parameters, allowing for the analysis of various scenarios and the identification of the most unfavorable outcomes [29]. Similarly, many other authors [30; 31; 32; 33; 34; 35; 36] stress the importance of risk assessment in estimating mineral reserves to minimize potential losses and improve the accuracy of long-term planning. Most studies address only one or two sources of uncertainty in the strategic planning process, which limits the impact of all relevant factors when estimating the economic value of a block in a geological model (the fundamental unit for optimizing mine boundaries and estimating mineral reserves). This approach can result in imprecise estimates and suboptimal decisions in complex mining projects. Therefore, this work aims to develop and validate a hybrid risk assessment model that integrates deterministic methods with stochastic techniques, in accordance with the principles of the ISO 31000-2018 risk management standard [37]. The objective is to thoroughly quantify the uncertainty of key parameters that affect the economic value of a block and enhance risk management in open-pit mining. To achieve this, the study focuses on implementing Monte Carlo simulations for financial risk assessment, integrating Value at Risk (VaR) and Conditional Value at Risk (CVaR) for precise risk quantification, and establishing a structured methodology applicable across different mining conditions. To implement the proposed hybrid model, Monte Carlo simulations are employed to generate a diverse set of potential scenarios, capturing uncertainties in key parameters such as metal prices, mining and processing costs, and geological variability. These factors directly influence the economic value of individual blocks and, consequently, the financial viability of the entire project. To enhance risk assessment, the model integrates Value at Risk (VaR) and Conditional Value at Risk (CVaR) as key indicators, quantifying potential financial losses and facilitating decision-making based on predefined risk tolerance levels. By systematically addressing uncertainty, this framework enables more reliable and informed strategic planning, ultimately contributing to improved long-term project profitability. In order to emphasize the practical benefits and validity of the presented model, a case study was conducted. The structure of this paper consists of five chapters, as illustrated in Figure 1. Section 2 offers a comprehensive overview of the methodology, focusing on developing a hybrid model that integrates deterministic and stochastic optimization approaches. Furthermore, it incorporates fundamental principles and key steps of risk assessment in accordance with the ISO 31000-2018 standard, ensuring a structured framework for identifying, analyzing, and evaluating risks in strategic mine planning. In Section 3, we identify sources of uncertainty for a specific case study involving a polymetallic copper, gold, and silver ore deposit and define the values and distributions for the basic input parameters necessary for implementing the model. Section 4 presents the results of our analysis related to the basic economic and technical optimization parameters and risk indicators. Finally, Section 5 discusses the overall results of the analysis, provides the conclusions of the paper, and offers recommendations for future research.