Spreadsheets were invented decades ago. 1980s saw the advent of electronic spreadsheet softwares that went on ahead to become the most popular tool for depicting business data and numbers, which ultimately led to financial modelling. The financial modelling gained popularity as it helped in representing rough calculation, in a neat tidy manner, to showcase business cases, impress the investors and take strategic decisions using What-If analysis. What was power point to marketing became excel to finance.

Though excel is widely available skill it is losing its sheen, with the advent of advanced forecasting tools that can handle Big Data and operate on large datasets. For now, excel has been replaced only where there is an issue of large data to handle. Excel still has the ‘humane connection’ which lacks in other data modelling software. Let’s figure it out who’s winning the war.

The ease, the flexibility, the visibility of excel surpasses that any black-box software technologies for a relatively smaller data set in excel. But this flexibility has caused spreadsheets prone to human errors.

“It has been reported that 88% of spreadsheets have some sort of error in them and that approximately 50% of spreadsheet models in use operationally in large businesses have material defects” – says the Dirty Dozen
ebook Dirty Dozen

This led to building of a standardized approach to financial modelling like FAST, SMART and other best practice methodologies were born in early 2000s.

The modelling standards helped revive confidence in excel and helped firms like Corality, Modano and F1F9 to grab a piece of the pie. Best practice modelling standard were developed in great detail to help reduce errors and still provide clear visibility of calculations – which was the original motive behind spreadsheets.

Still the human element is present, and the models built are still prone to errors especially when many hands are involved in a single build project. Regardless to mention the time and energy spent by the modellers to adhere to such standards is large.

The analytics softwares like R and SAS work primarily on statistics, to provide insights into the business processes, especially marketing and sales opportunities. The deal analysis, sensitivities and What-If analysis are better answered by the spreadsheet based financial models. As these models have greater visibility and easy to explain.

But the excel big data problems better handled by access, alteryx, etc, it has allowed excel to demarcate its position, and limit itself to processed and high-level data, which allows CFOs to work upon and chalk out strategies with respect to deal making.

With the availability of better top-line forecasting using statistical methods like regression and deep learning, the financial models of future will be relatively simple but still powerful enough to allow for accounting and customization at a short notice with minimal possibility of an error.

The forecasting tools will provide key inputs for the financial models and in turn these financial models will incorporate the necessary accounting calculations, provide financial statements and outputs necessary to woo investors and impress lenders keeping a customised touch.

Keeping these aspects in mind, being a software engineer too, I believe an Automation Engine, which can create such spreadsheet based financial models in seconds using a large database of templatised logics, will change the face of the financial modelling industry. Because financial models will no longer be built but created by the Automation Engine...

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