Financial Modelling & Analysis with AI

Domains and learning objectives

A domain’s weighting is its share of the discipline, and the share of the examination paper drawn from that domain. The study material and the examination questions are written to the objectives.

1. Domain 1

   Model Design and Spreadsheet Craft

   18%

   • 1.1Select an appropriate model structure, layout and level of granularity for a stated business question and audience
   • 1.2Apply consistent conventions for inputs, calculated cells, units, signs and time periods across a workbook
   • 1.3Construct formulae using lookup, aggregation, date and logical functions that remain correct when rows and periods are added
   • 1.4Distinguish robust formula patterns from fragile ones, including hardcoded values inside formulae, inconsistent ranges and hidden dependencies
   • 1.5Interpret error values, broken links and check-line failures to locate the underlying fault in a model
   • 1.6Evaluate a model against integrity criteria such as balance checks, flag totals, one-formula-per-row consistency and documented assumptions

2. Domain 2

   Three-Statement Model Construction

   22%

   • 2.1Construct linked income statement, balance sheet and cash flow logic in which every line item has a defined driver or schedule
   • 2.2Calculate working capital balances and their cash flow effects from days-based or ratio-based drivers
   • 2.3Build supporting schedules for fixed assets, depreciation, debt and equity, and link them correctly into all three statements
   • 2.4Distinguish causes of a balance sheet imbalance and select the correct diagnostic sequence to resolve it
   • 2.5Interpret circularity arising from interest on average debt or cash and select an appropriate treatment such as switch-controlled iteration or an opening-balance convention
   • 2.6Evaluate whether a completed model's cash, retained earnings and net debt movements reconcile with the underlying schedules

3. Domain 3

   Forecasting and Scenario Analysis

   18%

   • 3.1Select appropriate forecast drivers for revenue, costs and balance sheet items given the business model and available data
   • 3.2Calculate forecast values from growth rates, ratios, run-rates and simple regression or trend logic
   • 3.3Distinguish between base, upside and downside scenario design and sensitivity analysis, and select the right technique for a stated question
   • 3.4Construct scenario switches and sensitivity structures, including data tables, that change assumptions without rewriting formulae
   • 3.5Interpret sensitivity outputs to identify which assumptions genuinely drive the result
   • 3.6Evaluate a forecast's credibility against history, capacity constraints, market size and disclosed external benchmarks

4. Domain 4

   Valuation and Investment Appraisal

   20%

   • 4.1Calculate free cash flow to the firm from model outputs, adjusting correctly for depreciation, capex, working capital and tax
   • 4.2Calculate and interpret NPV, IRR and payback for a project or investment, including their conflicts and limitations
   • 4.3Construct a discounted cash flow valuation, including a weighted average cost of capital and a terminal value by perpetuity growth or exit multiple
   • 4.4Distinguish enterprise value from equity value and execute the bridge between them
   • 4.5Select and normalise comparable company multiples such as EV/EBITDA and P/E, and interpret differences across peers
   • 4.6Evaluate the sensitivity of a valuation to discount rate, terminal assumptions and forecast drivers, and identify which conclusions survive

5. Domain 5

   AI-Assisted Modelling, Audit and Assurance

   22%

   • 5.1Construct effective task specifications for an AI assistant to draft formulae, schedules or model documentation, including context, constraints and expected output format
   • 5.2Select appropriate uses of AI across the modelling workflow, distinguishing tasks where AI accelerates work from tasks where human judgement must lead
   • 5.3Interpret AI-generated formulae, code and explanations to detect logical, accounting and referencing errors before adoption
   • 5.4Distinguish AI failure modes, including fabricated figures, plausible but incorrect accounting treatments, stale knowledge and lost context in long tasks
   • 5.5Evaluate the confidentiality, data protection and governance implications of sending financial information to external AI services
   • 5.6Construct a verification workflow, combining check cells, reconciliation tests, independent recomputation and documented review, for any AI-assisted output