What are the most common financial modeling mistakes made by FP&A teams?

1 Hardcoding values

One of the most common financial modeling mistakes is hardcoding values, which means entering fixed numbers instead of using formulas or references to link cells. Hardcoding values can lead to inaccurate results, as it does not reflect changes in inputs or assumptions. For example, if you hardcode the tax rate as 25%, but the actual tax rate changes to 30%, your model will not capture the impact on your net income. To avoid hardcoding values, you should use formulas or references whenever possible, and clearly label and separate your inputs, assumptions, and outputs.

2 Inconsistent formatting

Another common financial modeling mistake is inconsistent formatting, which means using different fonts, colors, sizes, or alignments for similar elements in your model. Inconsistent formatting can make your model look unprofessional, confusing, or misleading. For example, if you use different colors for positive and negative numbers, but you switch them randomly in different sheets, your model will not convey the correct information. To avoid inconsistent formatting, you should follow a standard style guide for your model, and use formatting tools such as conditional formatting, data validation, or cell styles.

4 Lack of documentation

A lack of documentation is a financial modeling mistake that happens when you do not explain the logic, assumptions, or sources behind your model. A lack of documentation can make your model difficult to understand, verify, or update. For example, if you do not document how you derived your revenue growth rate, your model will not be transparent or credible. To avoid a lack of documentation, you should use comments, notes, or footnotes to annotate your model, and include a summary sheet that provides an overview of your model structure, purpose, and key findings.

6 Not testing your model

Not testing your model is a financial modeling mistake that means not verifying that your model works as intended and produces accurate and reliable results. Not testing your model can lead to errors, oversights, or inconsistencies that can affect your decisions and recommendations. For example, if you do not test your model with different scenarios, assumptions, or sensitivities, you will not be able to assess the risks and opportunities of your business. To avoid not testing your model, you should perform a quality control check of your model, and use tools such as scenario analysis, data tables, or charts to test your model outcomes.