Quantitative Risk Analysis: Expected Monetary Value, Monte Carlo, and Decision Tree - Systematic Literature Review
DOI:
https://doi.org/10.58857/JFAE.2026.v03.i01.p05Keywords:
Quantitative Risk Analysis (Quantitative Risk Analysis), Expected Monetary Value (EMV), Monte Carlo Simulation (MCS), Decision Tree Analysis (DTA), Systematic Literature Review (SLR)Abstract
Quantitative risk analysis (QRA) is an important approach to support decision-making under conditions of uncertainty, particularly in the fields of project management, business, and finance. This study aims to systematically analyze the development of the application of three main methods in quantitative risk analysis, namely Expected Monetary Value (EMV), Monte Carlo Simulation (MCS), and Decision Tree Analysis (DTA), and to identify research trends, advantages, limitations, and opportunities for integration of these three methods. The study uses a Systematic Literature Review (SLR) approach with reference to the PRISMA guidelines. The literature search process was conducted through the Scopus, Web of Science, ScienceDirect, Google Scholar, Portal Garuda, Neliti, and Indonesia OneSearch databases for the 2020–2025 publication period. From the results of the identification process of 84 articles, 12 articles were obtained that met the inclusion criteria and passed the quality assessment for further analysis. The synthesis results show that Monte Carlo Simulation is the most dominant method because it is able to model uncertainty probabilistically, while Expected Monetary Value is effective for decision-making with measurable probabilities, and Decision Tree Analysis excels in visualizing decision alternatives and their consequences. Furthermore, recent research shows a trend toward a hybrid approach that integrates EMV, MCS, and DTA to produce a more comprehensive risk analysis. This study also identifies three main gaps: limited integration between methods, minimal application in the information technology and digital finance sectors, and low utilization of advanced simulation software in the Indonesian context. The research findings are expected to serve as a reference for academics and practitioners in selecting and developing quantitative risk analysis methods that are appropriate to the characteristics of the uncertainty faced and encourage the development of more adaptive and integrated risk analysis models in the future.
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