Financial Modeling Tools

Learn about the various tools for financial modeling at your disposal

Financial modeling is an integral part of the investment process. Therefore, understanding the tools available is crucial for a modeler's success.


Finance professionals rely heavily on their robust knowledge of businesses and accounting. However, they tend to use only a few tools to get the job done. These include Excel, different programming languages, and various other applications that assist them in analyzing and processing data to value companies.

It may be surprising to learn that finance professionals use few tools when working with financial modeling, especially considering the amount and complexity of data they are required to analyze. However, the truth is - knowledge about financial principles and understanding how to interpret a business's numbers are more important than having a plethora of tools at your disposal. 

That being said, it is essential to understand the available tools thoroughly, as the few that are used are used very extensively.

Below we outline the tools that are used and what they are used for.


Even though there are four types of tools listed in this article, the vast majority of modeling is done on Excel.

Excel is a powerful tool, especially in the world of finance. It allows you to build a model from scratch or use templates from earlier, which saves time. This means that finance professionals can apply whatever format, structure, organization, or customization they prefer for their model. The best part is that they can then use the same settings, which is especially helpful when most transactions fall into certain buckets that require the same structure. The flexibility that Excel provides, along with its simplicity, is what makes it the most used tool among financial modelers.


Building a model from scratch also has other advantages. Many prefer building a model from scratch rather than using pre-programmed tools because the latter tend to be very rigid and unsuitable except across a very narrow use case.

Filling out all the company information in Excel can be a tedious process. However, it forces professionals to scrutinize the numbers and better understand the business (less autopilot work).

Accounting primarily involves the organization of information, something that Excel excels at.

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Excel also gives users the benefit of many pre-programmed shortcuts and formulas. These formulas and shortcuts that Excel offers are invaluable in finance. They are invaluable for financial modeling, making modeling much faster in many cases. In addition, if implemented correctly, they will require less effort to produce the required results.

Its price and universal usage are other reasons it is so commonly used. Excel is not prohibitively expensive and is accessible to most people. This makes it an ideal tool for many businesses and individuals. Its universal usage also cements its standing in the finance industry as everyone knows (or should know) how to use it.

Changing to a different program would likely be prohibitively expensive for companies. In addition, they would likely need to train employees to use it, which is unlikely to happen in the fast world of finance.

Another reason is that since most companies already use Excel, it would make it very hard to collaborate across companies, especially clients, which makes everything about the process much harder.


Its universal usage and low cost to access mean many free and paid resources are available online for learning Excel. This comes in the form of templates, Excel shortcuts, tips, and more.

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Programming Languages

While Excel may be the king of financial modeling tools, programming languages are frequently used, and it is a skill that employers look for in finance, especially in roles that require deep quantitative analysis.

The most commonly used languages for financial modeling include SQL and Python.

SQL is a language that is particularly suited for storing and retrieving data using organized databases. In addition, it is used for data retrieval and some data analysis. It is beneficial when working with large amounts of raw data as Excel is significantly slower with large amounts of data.


Python is a prevalent programming language both in and outside of finance. This is because it is one of the easiest languages to learn and is built for a comprehensive range of functions. It is also a very concise language, making it a very efficient language with little code needed to complete desired functions.

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Python is used for a wide range of tasks in finance, specifically in analyzing large datasets and in risk analysis. It can create probability distributions and run Monte Carlo simulations for sensitivity analysis. It can also be used to build discounted cash flow (DCF) and other models in the hands of a skilled programmer.


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Other Financial Modeling Software

Besides Excel and programming languages, many tools have been developed for financial modeling. Unfortunately, these softwares are chipping away at the market share of Excel and of other coding platforms, although they are still fairly rare. Below we outline two popular financial modeling software companies.



Quantrix Modeler is Quantrix's popular financial modeling software that offers several useful tools for financial modelers. It is used by thousands of companies, boasts 95% fewer formulas compared to Excel, and has zero reported spreadsheet errors.

This is a spreadsheet product similar to Excel, and building off of it means to allow people to apply their Excel skills to a program that is easier to use. This product attempts to give users the same flexibility and broad usage that Excel currently offers, but with fewer formulas and a better user interface. Its key advantage, compared to Excel, is that it is built for financial modelers and hence is faster and better at financial modeling.


Maplesoft is a company that provides programs for financial modeling, statistical modeling, and time series analysis. They also offer tools for option pricing, which includes Brownian motion graphing for the Black-Scholes method and lattice methods.

They also have tools for creating and running Monte Carlo simulations. Finally, they also provide tools for graphing yield curves under several different assumptions.

In addition, it has tools for pricing bonds with a variety of coupon rate types. Overall, they provide tools that can be used for many different purposes, some of which can be used to make financial modeling considerably easier.

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