The 12 Excel Functions That Drive 97% of Financial Models
Every great financial model – one that delivers accurate results while staying clear and easy to follow – is built with a remarkably small toolkit.
Excel isn’t just a spreadsheet; it’s a modelling language. And like any language, fluency comes from mastering a small set of core words and grammar patterns.
Between 2019 and 2025, our team reviewed over a hundred professional models across multiple sectors. We found something striking: twelve Excel functions accounted for around 97% of all formulas used in transaction-grade financial models.
In short, you can model like a pro by mastering twelve functions, not hundreds.
This article combines data and practical insight and forms the foundation for our Twelve Functions series, helping you:
- See which functions appear most often
- Understand how to deploy them effectively
- Know when to use alternatives
About the Models Analysed
The dataset covers models spanning a broad mix of sectors and purposes. Each model told a different story, but all spoke a common language.
They were developed using the DFT framework – Simplicity, Separation, Consistency, Linearity, and Integrity – with one additional PPS refinement: Modularity.
Together, these models draw on more than a decade of real-world experience and show how twelve well-chosen functions can carry almost any piece of financial logic. Whether we’re building an M&A model or working on budgeting and forecasting, it’s the simple stuff that does the heavy lifting.
Building on that foundation, here’s how the twelve functions are applied in practice.
What the Data Shows
When we analysed our models, a clear pattern emerged: across industries and purposes, the same small set of Excel functions did the work.
The below percentages refer to each function’s share of all unique formulas identified across the sample. Where newer, improved functions exist (for example, XMATCH replacing MATCH), both are grouped under the newer function.
Function Frequency in Financial Models
Together, these twelve functions dominate the formula landscape in high-quality models.
The remaining 3% are typically specialist functions – useful, but rarely essential.
Key Insights
- Even in large, complex project or fund models, more than 90% of all formulas come from just five functions.
- Logic functions (IF, AND, OR) appear in under 15% of formulas. They capture business rules clearly and efficiently, ensuring conditions are calculated once and flow through the model rather than repeated line by line.
- Lookup functions (INDEX, XMATCH, XLOOKUP) represent about 14% of all formulas. They form the connective tissue of the model, allowing data to move flexibly between sheets, timelines and modules.
- Volatile functions such as OFFSET and INDIRECT were entirely absent, reflecting a modern discipline focused on speed, traceability, and auditability.
The pattern is consistent: robust models depend on structure and discipline, not exotic formulas.
The dataset also highlighted what modern modellers leave out, and why.
Functions We Excluded
All models in this analysis were built to PPS’s internal standards of quality and transparency.
In client projects outside the study, however, we regularly review legacy or third-party workbooks that rely on OFFSET or similar functions to create shifting ranges and references.
While those approaches can work, they introduce volatility, slow calculations, and obscure audit trails – the opposite of what a disciplined model design aims for.
In every case, a cleaner solution has existed within the twelve-function toolkit. Structured use of INDEX, XMATCH, or SUMIFS achieves the same outcomes faster, more reliably, and with complete traceability.
That’s why these twelve form the benchmark set – not because they’re the only tools Excel provides, but because they’re the ones that consistently deliver traceable, maintainable models at scale.
The data tells a clear story, but its meaning goes beyond formula frequency.
The Model Behind the Model
Each of these twelve functions does one thing simply, but together they define how information flows through a model:
- Aggregation tells how much
- Logic decides when and whether
- Lookup determines from where
- Bounds constrain to what limit
- Time controls when it happens
The real discipline lies not in knowing every function Excel offers, but in knowing which ones to use – and where/when to stop.
Why These Twelve Still Matter
New tools and automation can speed up our work, but the fundamentals haven’t changed.
Understanding these twelve functions isn’t about memorising syntax; it’s about mastering structure, logic, and transparency.
They form the shared language of every scalable, auditable financial model.
Master them, and your models will evolve from one-off tools into lasting systems that continue to add value over time.
Closing Thought
As modellers, we need to remember that the tools we build are rarely used by people with the skillset of a modeller, or the time to unpack complex workings.
Our responsibility doesn’t end with accuracy – it ends with understanding. If a model isn’t easy to use, we’ve failed and haven’t done our job successfully.
The purpose of a financial model is not to impress, but to inform.
A model that only its author understands isn’t a model, it’s a risk. Every formula, structure, and label should exist for one reason: to help someone else make a better decision.
That’s why accuracy alone isn’t enough. A well-built model should explain itself. It should invite confidence, not require interpretation.
Because when understanding breaks down, even perfect calculations lose their value. And when it holds, the model becomes what it was always meant to be, clear, dependable, and understood.
So the next time you build or review a model, ask yourself: would someone else pick it up and make the right decision without needing to call you? If the answer is yes, you’ve succeeded. If not, there’s work to do.
At PPS Financial, our approach to financial model build and review services is grounded in structure and real-world usability. We help teams create scalable, auditable models that stand up to investor scrutiny and internal decision-making alike.
If you’d like to strengthen your team’s modelling capability, get in touch with PPS to discuss tailored support.
Methodology Footnote
Source: PPS analysis of anonymised professional financial models built 2019–2025.
The dataset includes models developed for project finance, real estate, SaaS, LBO, refinancing, acquisition, sell-side, valuation, and DCF analysis, as well as corporate forecasting and training exemplars – each reflecting real-world transaction and decision contexts.
Models were selected for structural quality, audit transparency, and performance.
Function frequencies were calculated by cataloguing all unique formulas across the dataset.
Percentages are rounded to the nearest whole number.
All models were developed or reviewed by the PPS team to ensure consistent structure and formula hygiene.
