An AI assistant that helps financial analysts build complex financial models. It can generate formula structures, create multi-year forecasts based on assumptions, and explain the logic behind the calculations in plain English.
Building a financial model from scratch is slow. Analysts set up tabs for assumptions, revenue build, costs, cash flow and valuation, write and check hundreds of formulas, and document the logic so others can follow it. Smaller finance teams often inherit models nobody fully understands, where one broken reference quietly skews every forecast. Business owners want scenario answers quickly, but each new question means hours in the spreadsheet.
An AI financial modelling assistant helps with the mechanical parts. Given a description of the business and the assumptions, it can scaffold a model structure, write formulas, generate multi-year projections, and explain in plain English how each figure is derived. It can also review an existing workbook and point out hard-coded numbers, inconsistent formulas and circular references. The analyst remains responsible for the assumptions and the conclusions; the assistant speeds up construction and makes models easier to audit.
The analyst describes the model needed, the business drivers and key assumptions, or uploads an existing workbook for review.
An LLM with code interpretation drafts the structure, writing formulas directly into Excel or Google Sheets through their APIs or generating the workbook with Python.
Assumptions are kept on a separate input sheet, so scenarios can be changed without touching formulas.
The assistant runs checks for errors, balancing issues and hard-coded values, and writes a plain-English explanation of each section.
The analyst reviews every formula and assumption, tests scenarios, and signs off the model before it informs any decision.
An analyst prompts the AI: 'Build a 5-year discounted cash flow (DCF) model for a SaaS company with a 30% annual growth rate.' The AI generates the complete Excel or Google Sheets model, complete with all necessary formulas and a summary of the valuation.
LLMs with code interpretation (GPT), Spreadsheet APIs, Python for finance libraries
Models should reflect Australian tax settings: GST treatment, the applicable company tax rate for your entity, compulsory superannuation contributions on wages and payroll tax thresholds that vary by state. Check these rather than accepting the model's defaults, which may assume overseas rules. If forecasts are shared with investors or lenders, directors remain responsible for their reasonableness, and misleading forward-looking statements can raise issues under the Corporations Act and ASIC guidance. Uploading confidential financials to an AI service should follow your data policy; choose enterprise tiers that do not train on your data and, where required, keep data in Australian cloud regions.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
It can produce a working first draft, including formulas and a summary, from a clear description. It cannot know your business well enough to set sound assumptions, and it can make formula errors. Treat the draft as a starting point that a qualified person checks line by line before using it for budgeting, lending or valuation decisions.
Yes. The assistant can read an existing workbook, explain what each sheet does, find errors and suggest improvements. This is often the most valuable use, since many businesses rely on inherited models that nobody fully understands. Very large or heavily macro-driven workbooks may need to be reviewed in sections.
The two-to-four-week band covers choosing an approved AI tool with appropriate data protections, connecting it to your spreadsheet environment, creating model templates and prompts suited to your business, and training the finance team. Costs mainly come from AI licences and the time spent validating templates.
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