What is a financial model and why do I need one?

A well-designed, well-built, and correctly used financial model can be unbelievably powerful for optimising processes

Trying to describe what a financial model looks like, and indeed what its purpose is, can be tricky. And yet, a well-designed, well-built, and correctly used financial model can be unbelievably powerful.

A financial model is a tool designed to aid decision making and a good financial model will increase the quality and quantity of information available to managers, which in turn enables them to make more informed decisions for their businesses and organisations. Better decisions lead to a more efficient use of resources, boosting productivity and profitability.

How do financial models drive an improvement in information? A model that is properly scoped and designed will:

  • Be quick and easy to update and refresh – the role of the user shifts from spending their time updating the model to analysing the model outputs and translating the data into actionable tasks.
  • Be consistent – best practice models will have consistent structure and formatting; for example, distinguishing between assumption and calculation sheets, a centralised time series with the same columns on each sheet, and the alignment of the sheets being uniform throughout the model. This not only makes the model look more professional, but it makes models far easier to use.
  • Be robust – a well-built model will be robust, greatly reducing the chance of user error. This is achieved by using error and alert checks, that will notify the model user when errors are present in the model, and best practice formula creation. For example, ensuring that the model will return “0” if a divisor is 0, rather than causing an error. For an additional level of robustness, best practice models can be protected in such a way that only assumption data cells can be changed, with all other data points “locked-down”. With a robustly designed model, greater confidence can be placed in the model’s outputs, reducing the risks to the business.
  • Be transparent – removing hardcoding in formulas, hidden rows and sheets, simplifying formulas and using purpose-based formatting ensure greater understanding in the model. A transparent model is easier to handover between team members, reducing key person risk in your organisation.


Consistency and transparency reduce key man dependency and creates a framework through which team members can quickly understand their colleagues’ models. The models’ flexibility and robustness mean fewer hours spent redeveloping and repairing existing models and more time spent allowing team members to focus on the analysis and the outputs of the models.

There are many off-the-shelf “financial models” available online, often at an extremely low cost. However, they overlook one crucial element of modelling an organisation – the commercial understanding. A model is not simply a well formatted spreadsheet. It is borne out of a deep understanding of how that organisation operates: the key drivers of the revenue and expenses, the challenges and opportunities facing the organisation, and the users of the model. It should not only reflect where the business is today but where it is heading in the next few months and years.

There is a seismic gap between building a generic spreadsheet and a financial model built specifically for a business; a correctly built financial model is bespoke for that specific organisation, with the key personnel involved throughout the process.

Recent advances in technology have also taken the capabilities of financial modelling to new heights. Microsoft Excel now has the ability to efficiently handle database tables, which can be millions of rows long, in a file that is no bigger or slower than a standard Excel file. Model outputs can also now link to visualisation tools such as Power BI and Tableau, meaning model outputs can be viewed in real-time on the web and on mobile devices. The line between financial modelling and data analytics is blurring, indeed there is now ample crossover between the two disciplines. At Forecast, we term this “Commercial Analysis”.

The skillset and background of modelling versus analytics teams remains distinctive. Modellers are likely to have background in banking, accounting or other commercial roles, whereas data analysts are more likely to have experience in statistics, software design, and academia.

The tools of  modellers versus data analysts are also different. Modellers will typically use spreadsheet software, such as Microsoft Excel, that is both in widespread use and well understood by organisations. Data analysts will use open-source programming languages such as R and Python, as well as proprietary languages such as SQL and SAS, to query large amounts of data.

However, with the two disciplines complementing each other, combined engagement teams are extremely common, providing an extremely powerful range of skills and experiences. Here at Forecast, we have highly skilled and experienced modellers and data analysts to ensure that all possible requirements and scenarios are met in your engagement.

If you feel your business or organisation can optimise its financial planning processes, and want to unleash the power of best practice financial models and analytics tools, please do not hesitate to get in touch.


Like this article? You’re one step closer to making data-driven decisions.

Reach out to us to start a conversation – we welcome an opportunity to understand your data needs, challenges and aspirations. We utilise an expert toolkit of data capabilities and services to help take guesswork out of your decision making.

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