Excel vs Automation in Financial Modeling

Learn about the different tools used in financial modeling

What is Excel vs Automation in Financial Modeling?

Before we discuss Excel vs automation in financial modeling, let’s take a look first at building financial models. Financial modeling involves creating an abstract representation of an actual financial event. Thus, a financial model is a mathematical tool that is incorporated in MS Excel to help in designing the abstract model.

 

Excel vs Automation

 

Financial modeling is important to businesses because it predicts how a company is likely to perform financially in the future. To predict financial performance, a financial modeler utilizes the company’s historical performance while making a few assumptions about the future.

There are two ways of doing financial modeling. The traditional way involves using MS Excel spreadsheets. However, in modern times, several companies are starting to embrace the concept of automating the task of financial modeling.

 

Excel vs Automation – Building and Designing Financial Models

When designing financial models, one needs to make many tradeoffs between using Excel and other software that automates most tasks. Financial analysts and other users of financial information continue to debate on which one is better. Let’s see the differences between the two:

 

1. Customization

Given that Excel is manned by a human modeler, it offers a great deal of customization. The fact that Excel allows one to design a model from scratch means that this individual enjoys a lot of freedom in structuring it the way he wants. Also, he can format the model based on the needs of the business he is modeling for.

For example, if there are some asset-specific aspects that need addressing in the model, that can only be achieved using Excel. In contrast, using financial modeling software limits the scope of customization, as it is already pre-programmed.

 

2. Structured outcome

The best time to use financial modeling software is if one is dealing with a specific structure of a model. in such a way, the human modeler can reduce the likelihood of making errors as most of the tools he will use are programmed to prevent errors.

On the other hand, the likelihood of making errors when using an Excel model is higher than with an automated system. So, if there’s an emphasis on standardization and accuracy, the better approach to use is a financial modeling software.

 

3. Development of analytical skills

However, if a modeler is more interested in understanding his business than being accurate, an Excel model is much better. This is because using Excel involves going through the painstaking process of calculating pretty much everything. Although tedious, the act of computing different financial records really helps one to understand the business better.

Now imagine that the modeler was using a software program instead. The program would use the company’s financial statements, capital structure, and forecast, and then reveal a net present value spontaneously. Even though it saves time, it would not teach the modeler a single thing about the business.

 

4. Risk analysis

Although software doesn’t enlighten a modeler, there is one area it is good at, and that is in risk management. An Excel model is limited in managing risks. Even though one can run a sensitivity analysis on Excel, the whole process will be completely manual; hence, increasing the probability of making errors. Risk analysis methods like sensitivity analysis and Monte Carlo simulation are more easily performed on financial modeling software, which offers higher precision.

 

5. Logical interpretation

To be able to forecast accurately, a financial modeler needs to apply logic, and this is only possible in Excel. With an Excel spreadsheet, the analyst can examine a company’s data, make certain assumptions, study the relationship between financial statements, and ultimately compute the formula.

There’s a need to assess the relationship between the dependent variables in order to apply logic, and that can be very hard to achieve when using software. Since certain financial modeling software uses built-in tools of logic, it’s not possible to analyze the flow of individual operations.

 

6. Visual representation

Visual representation makes up an important part of financial modeling. To represent the financial results of a company using easy-to-understand charts, one will need to use advanced software like Operis or Analytica. Thus, financial modeling programs are better than Excel in representing data in graphical form.

 

7. Handling complex data

Although Excel has proven to be an effective method this far, there are a few areas where it falls short, particularly in handling complex sets of data. However, with financial modeling software, one can compute multidimensional and large sets of data without any difficulty. Most programs allow modelers to create and switch the rows and columns layout of the model based on the situation at hand.

 

Final Word

Whether financial modeling should be automated or not will depend on individual company needs. For example, for small businesses that deal with small data sets and don’t need very structured outcomes, using Excel is better because it gives the owner insight into his operations.

However, if a company is dealing with diverse and large data sets or is more interested in precision and risk management, then automating its financial modeling task is the best option.

 

Additional Resources

CFI offers the Financial Modeling & Valuation Analyst (FMVA)™ certification program for those looking to take their careers to the next level. To keep learning and advancing your career, the following CFI resources will be helpful:

  • Documenting Excel Models Best Practices
  • Financial Modeling Best Practices
  • Risk Management
  • Sensitivity Analysis

Financial Modeling Certification

Become a certified Financial Modeling and Valuation Analyst (FMVA)® by completing CFI’s online financial modeling classes!