What you may like:
What you may NOT like:
For users with an extremely limited budget, RiskAMP is the only available option, but it does not meet professional quality standards. Oracle Crystal Ball is an older product with limited functionality, and its development appears to have stagnated. Based on our evaluation, it is difficult to use and lacks the capabilities found in more advanced risk analysis tools.
For those with a budget, the best choices are ModelRisk, Analytic Solver, and @RISK. ModelRisk stands out as the most advanced and cost-effective risk modelling add-in for Excel. If stochastic optimization is a priority, Analytic Solver is the best option. Additionally, while not covered in this review, Frontline Solvers offers a Data Mining tool that can complement Analytic Solver’s optimization capabilities, though this significantly increases costs.
In comparison, @RISK is priced significantly higher than its competitors, largely due to its early market presence and established user base. The primary reasons to choose @RISK are either an existing perpetual license or a requirement to maintain compatibility with existing models.
Spreadsheets are widely used to estimate future values, such as the total cost of a project, the net present value (NPV) of an investment, or a sales forecast. These estimates are often derived from a series of calculations spread across multiple sheets. However, a key concern is understanding the level of confidence in these single-point estimates.
A risk analysis add-in enhances spreadsheet models by replacing static input values with functions that generate random values based on the uncertainty associated with each input. This technique, known as Monte Carlo simulation, allows users to define output cells and run thousands of simulations to analyse the range and probability distribution of possible outcomes. By doing so, it provides valuable insights into the confidence level of the results. Excel remains a powerful platform for such models due to its flexibility and ease of use. This article provides a more detailed explanation of the Monte Carlo simulation process.
Many users of Monte Carlo add-ins for Excel rely on a single product, often using it for years without exploring alternative options. Given the significant investment in these tools, it is valuable to periodically assess what else is available in the market. This review aims to facilitate such comparisons by providing an objective evaluation of the leading risk analysis software solutions.
Consider the complexity of your models. If your analysis involves basic cost summation, most add-ins will suffice. However, specialized fields often require specific probability distributions—engineers use Weibull, epidemiologists work with hypergeometric distributions, and financial analysts may need log-normal distributions. Similarly, advanced correlation structures and time series models should be available in the software you choose.
While modern computing has made simulation speed less critical, certain scenarios require efficiency. High-precision tail probability calculations, such as those in banking capital allocation models, may demand large sample sizes (e.g., 500,000+). Similarly, stochastic optimization, like portfolio risk analysis, requires extensive re-runs, making simulation speed an important factor.
Depending on your use case, you may need advanced distribution functionalities. Features like convolution (summing distributions in a single cell), extreme value analysis, and reverse convolution (dividing one distribution by another) can significantly enhance model efficiency.
A user-friendly interface can greatly enhance workflow efficiency. At a minimum, an intuitive visualization of probability distributions is essential. Interactive features, such as sliders for probability exploration, improve usability and understanding.
All reviewed products use the Mersenne Twister RNG, a widely accepted standard. However, more critical than the RNG itself is how the software transforms random values into valid samples. Features like seed control ensure reproducibility, and scenario analysis helps track rare outcomes. Stratified sampling methods, while often touted, provide little benefit unless dealing with very few distributions.
If your organization already uses a specific tool, it may be easier to adopt the same one for compatibility reasons. However, cost savings from switching should be considered, particularly for large-scale deployments.
Reliable technical support is crucial, particularly as Excel updates can impact add-in functionality. IT security changes within an organization may also affect installations and updates. Testing a vendor's support responsiveness with a trial inquiry can be beneficial.
High-quality example models and training resources can accelerate adoption. Some providers offer online courses, consulting packages, or on-demand training, which can be valuable for those new to risk analysis.
Effective reporting tools streamline the process of integrating model outputs into presentations and reports. The ability to quickly update reports with revised assumptions is particularly useful for last-minute changes.
A trial version is essential for evaluating software before purchase. All reviewed products offer trial versions, usually limited to 15 days. ModelRisk offers a unique feature where it transitions into a basic version after the trial period expires, allowing continued use with some limitations.
Software is increasingly moving towards cloud-based solutions. Understanding a vendor's long-term development plans can help ensure that your choice remains viable as technology evolves.
Price is a key factor in decision-making. A detailed comparison of costs and value metrics can be found in the table at the end of this article.
Products are listed alphabetically.
Developed by Frontline Solvers, Analytic Solver is a powerful add-in that combines Monte Carlo simulation with optimization capabilities. Its strength lies in its ability to perform stochastic optimization, allowing users to define decision variables and optimize stochastic results such as risk-adjusted profitability metrics.
While Monte Carlo simulation is just a part of the overall toolset, Analytic Solver is known for its exceptional speed, benefiting from its developer’s deep knowledge of Excel’s calculation engine. Additionally, the software includes features such as AI-driven assistance and web publishing options.
Originally developed by Decisioneering, Inc., Crystal Ball was among the first Monte Carlo simulation tools for spreadsheets. Later acquired by Oracle, the product has seen little development since. Its approach of overlaying uncertainty onto spreadsheet values rather than modifying cell formulas makes model-building less intuitive compared to modern alternatives.
ModelRisk was designed to address the limitations of early risk analysis tools by introducing advanced statistical features such as time series, copulas, and extensive distribution fitting options. It remains one of the most feature-rich Monte Carlo simulation tools available, offering a unique Results Viewer that enhances data analysis and reporting.
As one of the earliest Monte Carlo add-ins, @RISK has built a strong user base over the years. It includes a broad range of features comparable to ModelRisk. However, it comes at a significantly higher price and lacks some of the more technical functions available in competing products.
RiskAMP is the most budget-friendly option but offers a minimal set of features, making it suitable for educational purposes rather than professional risk analysis.
Here, we take a closer look at key features to highlight the differences between Monte Carlo add-ins.
This section examines how different tools approach distribution fitting, a critical aspect of risk modelling. The same dataset was used across all tools to illustrate variations in interface design and technical methodology.
Certain distributions, such as the Uniform, are not well-suited for fitting to data as they assume a strict minimum and maximum based on observations, which can be misleading if based on a small sample. Good software should guide users away from such assumptions.
Another key consideration is parameter stability. A small sample size can lead to significant variations in estimated parameters, which is why advanced tools use parametric bootstrapping to account for parameter uncertainty.
Lastly, statistical methods like the Akaike Information Criterion (AIC) help prevent overfitting by adjusting for the number of parameters in a distribution. ModelRisk pioneered the use of AIC in risk analysis software, with @RISK and Analytic Solver adopting it later.
Analytic Solver provides a robust algorithm for distribution fitting. However, it does not allow referencing source data inside the function, nor does it incorporate parameter uncertainty into the model.
Crystal Ball relies on older fitting statistics (e.g., Anderson-Darling, Chi-Squared) instead of modern Information Criteria, leading to potential overfitting with multi-parameter distributions. Like Analytic Solver, it does not allow referencing the source data in the function and does not account for parameter uncertainty.
ModelRisk applies Akaike Information Criteria (AIC) and supports Likelihood Ratio (LR) comparisons. It allows referencing the source data directly within functions and incorporates parameter uncertainty, shown via multiple fit lines. A fitted Normal distribution linked to the data appears as:
=VoseNormalFit(C3:C423)
@RISK employs a solid fitting algorithm but does not consistently apply the same criteria across distributions. It allows referencing source data in the function but does not incorporate parameter uncertainty into the model itself, only displaying it in the fitting interface. Mode estimation in @RISK should be disregarded as it lacks statistical relevance. A fitted Normal distribution looks like:
=RiskNormal(2.1785,3.052,RiskFit("Dataset 2","AIC"),RiskName("Dataset 2"))
There are significant differences in how each product allows users to access and present simulation results. This section provides an overview of those differences.
After running a simulation, Analytic Solver allows users to navigate through input and output cells, where thumbnail graphs appear. Clicking on a thumbnail opens a full-sized, editable chart. Users should be mindful to remove the default 3D effect for clearer visualization.
Oracle Crystal Ball generates multiple pop-up charts upon simulation completion. While the visuals are aesthetically pleasing, managing the large number of charts and specifying outputs can be challenging.
ModelRisk includes a dedicated Results Viewer, which can remain open during simulations or update dynamically upon completion. Users can create multiple tabs, each containing charts or tables for different inputs, outputs, and simulation runs. The viewer allows full customization, direct exports to various file formats, and retains settings within the Excel file, ensuring automatic graph regeneration upon model updates.
When a simulation is running in @RISK, a results chart appears, updating dynamically. Users can navigate outputs using ALT+right arrow, and small icons allow switching between graph types. After completion, graphs are accessed through the Reports dropdown, requiring multiple steps to generate visualizations. The process of managing multiple open windows can be cumbersome compared to an integrated results viewer.
Decision trees visually represent a sequence of decisions and chance events. While historically used in decision analysis, their practical application has largely been replaced by Monte Carlo simulation, which offers greater flexibility, realism, and ease of implementation. Despite this, some risk analysis add-ins still include a decision tree component, although their utility in Excel is questionable.
Excel is not inherently designed for graphical modelling, making decision tree implementation within the platform cumbersome. Both Analytic Solver and @RISK (via their PrecisionTree product, not reviewed here) attempt to integrate decision trees, but the limitations are apparent. Below is an example of a decision tree created in Analytic Solver:
The main issue with decision trees in Excel is that while calculations are performed within cells, the graphical lines connecting decisions and outcomes are simply drawn on top. This makes the structure prone to distortion, as shown in the following example where lines have been unintentionally moved:
Given these limitations, Monte Carlo simulation is generally a more robust approach for analysing decision scenarios within Excel.