Financial Modeling - Build or Adapt

The Evolution of Financial Modeling

Financial modeling, as a profession, has developed at an incredible pace, especially in the past two decades. Previously classified under financial analysis, financial modeling now stands out as a highly technical and quantitative skill which extends into many arrays of approaches, complexities, practices and other industry standards that keep changing.

The skills set required by a capable financial modeler is so diverse that retaining such talents has become expensive. Institutions have tried to build and sustain operating models in-house, but the market dynamics keep forcing them to outsource to financial advisors again and again. It was worth the try though when a "spreadsheet" is costing you over $1 million to outsource!

Efforts have been made to standardize and unify modeling practices to reduce the dependence on outsourcing, by minimizing the role of advisers to hopefully model audit and verification. This has succeeded in many sectors and projects.

The Core Question: Build or Adapt?

The question facing any CFO or FP&A Manager, when appointed with a new modeling task, would be: Do we build it from scratch or do we adapt to an existing model?

The Human Factor

Before we dig deep into the pros and cons, we have to remember that we are dealing with lots of human judgement and bias towards background, experience, workstyle, time pressure and many other factors facing the modelers put to the task. And yes, that fear of being accused of "re-inventing the wheel" always clouds such debates.

As a manager, you want less risk, even if you sacrifice some innovation in building a new model, but rather you want to deliver on time. Therefore, CFOs with no modeling background might always encourage adapting to an existing template. They might not know that despite project/sector similarities, it sometimes might actually take less time to build from scratch rather than fully understand and comprehend the mechanics of an existing template.

SWOT Analysis: Adapt vs. Build

Below is a SWOT comparison for adapting to an existing template versus building a model from scratch.

Strengths

Adapt to an Existing Template	Build a Model from Scratch
Time efficient when replicating a case	Proprietorship over the job
Acceptability by financial institutions or top management accustomed to the template	Motivational for modelers
Verified accuracy of calculations	Induces learning, team work and experience
Cheaper/faster model re-audits	Fosters creativity and new ideas
	Customized flexibility
	Every calculation is relevant

Weaknesses

Adapt to an Existing Template	Build a Model from Scratch
Sector/project relevance could impact the model quality/relevance	Time consuming
	Resource costly

Opportunities

Adapt to an Existing Template	Build a Model from Scratch
Multiple engagements can be achieved simultaneously when bidding projects or carrying out tasks	Exceptional quality can be demonstrated
Cost savings potential	A feel of dedication and due diligence for the case in hand can give credibility
Time saving potential	A new and better template could be in the making for the future
	Capitalizing on new spreadsheet capabilities
	New features can be added quickly due to proficiency with the hand-built model structure

Threats

Adapt to an Existing Template	Build a Model from Scratch
Failure to adapt new model needs to the existing template	Failure to deliver on time
Sacrificing model quality/detail by scaling down to current template dimensions/capacity	Tension and de-motivational threats or even job security could arise
Model integrity can be impaired due to either short-cuts required to scale down details or side calculations required to make-up for lack of details	Wrong selection of the capable modeler for the job
Limited ability to conduct further or special changes required by auditors, investors or financial institutions

The Verdict: It Depends

Well, to conclude, there is no conclusion! It will always be a case by case situation depending on:

• The background of the manager in charge
• The capabilities of the modeler(s) in charge
• The nature of the new venture or task
• The needs of the recipients of the model

And probably other factors too.

What About AI?

Another question comes to mind: what about AI, could it relieve us from all of that? Asking an engine to produce a model — probably yes, but that would work for simple business plans, at least for now. Let's see what the future holds.

Imagine hypothetically that AI took over this skill. If this were the case, then it would come down to a game of parameter inputs. But as long as modeling continues to be a philosophy in dealing with figures, using judgment and channeling feedback to operations to optimize parameters, then the industry can remain immune to being taken over by AI.