Application of probability theory in risk analysis for optimal business decision making

Abstract

This study discusses the application of probability theory in risk analysis to support more optimal business decision making amidst market uncertainty. The methods used include probability distribution, Bayes' Theorem, and Monte Carlo Simulation to identify and measure risks in various business scenarios, including market volatility, investment decisions, and profit projections. This approach allows quantitative modeling of uncertainty, so that risks can be evaluated more accurately and measurably. The results show that risk analysis based on probability theory provides deeper insight into possible business outcomes, enables more effective risk mitigation, and improves the accuracy of strategies in dealing with market changes. Based on the analysis results, it can be concluded that the application of probability theory plays a significant role in managing business risks and uncertainties. Monte Carlo simulation, Bayes' Theorem, and probability distribution analysis allow companies to make more accurate predictions regarding future events, leading to more data-driven decision making. In addition, a probabilistic approach enables businesses to clearly identify patterns of risk and opportunity, optimize resource management, and enhance operational, marketing, and investment planning strategies. However, challenges remain in its implementation, especially regarding the understanding of probability concepts and the need for accurate data. Therefore, stronger integration between probability theory and analytical technology is essential to optimize risk management strategies. With the right approach, probability theory becomes a valuable tool for supporting sustainable and competitive business growth in a dynamic market environment