What are the best practices for ensuring financial model accuracy and reliability?

1 Follow consistent standards

One of the best practices for ensuring financial model accuracy and reliability is to follow consistent standards for formatting, naming, structuring, and referencing your financial model. This will help you avoid errors, confusion, and inconsistencies, as well as make your model easier to understand, review, and update. Some of the common standards for financial modeling include using clear and logical labels, separating inputs and outputs, using consistent colors and fonts, applying formulas consistently, and using absolute and relative references appropriately.

2 Apply quality checks

Another best practice for ensuring financial model accuracy and reliability is to apply quality checks throughout the modeling process, as well as before and after using your model. Quality checks are methods of verifying and validating your model's inputs, outputs, calculations, and logic, such as using error checks, cross-checks, sanity checks, and reasonableness checks. Quality checks can help you identify and correct any mistakes, errors, or anomalies in your model, as well as ensure that your model is consistent with your objectives, assumptions, and expectations.

3 Document assumptions

A third best practice for ensuring financial model accuracy and reliability is to document your assumptions clearly and transparently. Assumptions are the inputs, parameters, and factors that you use to build your model and estimate your outputs, such as growth rates, discount rates, margins, and multiples. Documenting your assumptions can help you justify and explain your model's results, as well as track and update any changes or revisions. You can document your assumptions by using a separate sheet, a table, or a footnote in your model, and by providing sources, references, or explanations for each assumption.

4 Test sensitivities

A fourth best practice for ensuring financial model accuracy and reliability is to test your model's sensitivities to various changes in your assumptions, inputs, or scenarios. Sensitivity analysis is a technique of measuring how your model's outputs vary depending on the changes in one or more of your model's inputs, such as changing the growth rate, the discount rate, or the exit multiple. Sensitivity analysis can help you assess the robustness, reliability, and uncertainty of your model, as well as identify the key drivers and risks of your model's outcomes.

5 Use data validation

A fifth best practice for ensuring financial model accuracy and reliability is to use data validation tools to limit and control the type and range of data that you enter or select in your model. Data validation is a feature of spreadsheet software that allows you to set rules and criteria for the data that you or others can input in your model's cells, such as numbers, dates, lists, or formulas. Data validation can help you prevent errors, inconsistencies, and outliers in your model's inputs, as well as ensure that your model's calculations and outputs are based on valid and relevant data.

6 Review and audit

A sixth and final best practice for ensuring financial model accuracy and reliability is to review and audit your model regularly and thoroughly. Reviewing and auditing your model is the process of checking, testing, and evaluating your model's structure, logic, formulas, data, and results, as well as identifying and fixing any issues, errors, or improvements. You can review and audit your model by using built-in or external tools, such as formula auditing, error tracing, data tables, scenario manager, or Excel's Inquire add-in. You can also ask someone else to review and audit your model, such as a colleague, a supervisor, or a third-party expert.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?