The honest first answer to "which financial modelling software should we buy" is usually: the spreadsheet you already own, used better. Excel remains the lingua franca of financial modelling for good reasons — universal, auditable, infinitely flexible — and most modelling failures are method failures no tool would have prevented. But the dedicated tools have earned real places at specific jobs: driver-based forecasting that non-finance managers can touch, three-statement models that do not break when someone inserts a row, and scenario work at a speed spreadsheets cannot match. Here is how to think about the choice.
The landscape, by job to be done
"Financial modelling software" covers four different product categories, and mismatching category to job explains most buyer disappointment:
- Spreadsheets (Excel, Google Sheets) — still the right tool for transaction models, valuations, bespoke one-off analysis and anything an outside party (lender, investor, buyer) must interrogate: everyone can open it, and diligence teams distrust black boxes;
- FP&A and forecasting platforms — the cloud tools that connect to your accounting system and turn budgeting, rolling forecasts and scenario planning into a managed process rather than an annual spreadsheet death-march. Their genuine advantages: live actuals, driver logic, version control, and departmental managers entering their own numbers without breaking formulas;
- Reporting and dashboard layers — tools that sit on the ledger and present, which buyers frequently purchase believing they bought forecasting; check which you are being sold;
- Specialist engines — project finance, real estate, consolidation-heavy group models: narrow tools for narrow, deep jobs.
The selection questions that actually discriminate: does it integrate with your ledger (a platform your bookkeeping system cannot feed is a rekeying machine); who maintains the model logic when the builder leaves; can it produce the three statements coherently or only a revenue line with ambitions; and can outsiders audit it when the model becomes the basis of a fundraise or sale.
What no software fixes
The failure modes we see in client models are tool-independent: forecasts built on last year plus a percentage, with no drivers anyone believes; models with no balance sheet, so cash surprises everyone (a profit forecast without working capital is a fiction with formatting); scenario "analysis" that is one optimistic case renamed; and the classic — a beautiful model nobody reconciles to actuals, so its errors compound privately. The disciplines that matter are old ones: separate inputs from logic from outputs, document assumptions where a stranger can find them, reconcile to actuals monthly, and version-control anything that feeds decisions. A £50-a-month platform enforcing those habits beats a £50,000 model that ignores them; so does a well-built spreadsheet.
A pragmatic selection path for an SME
The sequence that avoids both under- and over-buying: first, fix the chart of accounts and management reporting — modelling on top of messy actuals automates confusion; second, decide whether the need is reporting (seeing what happened), forecasting (a live view forward), or transaction modelling (a one-off analysis for a raise, purchase or exit) — they want different tools and often different people; third, pilot with one real use case — next quarter's forecast, not a greenfield five-year plan — before committing the organisation; and fourth, budget for the modelling skill alongside the software, because the tools amplify whoever operates them, in both directions. For transaction work specifically, stay in the spreadsheet and invest in the build quality: lenders' and buyers' teams will take the model apart cell by cell, and its credibility becomes your credibility.
When to bring in help
The build-versus-buy-versus-hire question tends to resolve by frequency: a business that models occasionally (a raise, an acquisition, an annual budget) is usually better served commissioning the model than owning a platform; a business managing itself through rolling forecasts should own the platform and the habit. Acumon's financial modelling team builds the transaction-grade models — three-statement, scenario-capable, diligence-ready — and our cloud accounting and management accounts practices set up the forecasting stack for businesses making it a discipline, with fractional FD support where the missing ingredient is the person who asks the model hard questions. The software market will happily sell you any of it; the value is in matching the tool to the decision it serves.