Verifiable cash flow forecasting

How can you use AI to build a weekly cash flow forecast whose assumptions and calculations remain verifiable?

1960 € per session, excl. VAT

In short

This seven-hour day, excluding breaks, enables you to build a thirteen-week cash flow forecast, from payment schedules to liquidity stress scenarios. Generative AI helps structure assumptions and checks; calculations are verified in a spreadsheet and conclusions are submitted for validation by a qualified finance professional.

Why this training?

A positive accounting result does not indicate the cash available in bank accounts. Incorrectly scheduled customer payment dates, omitted expenses or double-counted cash flows can conceal liquidity pressure and distort the assessment of financing needs. Generative AI can propose a model structure, clarify assumptions and suggest tests. It replaces neither source documents nor recalculation: the training separates writing assistance, financial calculation and professional judgement. Running case study: The fictitious company Neroval Composants needs to finance an inventory purchase while two customers delay their payments. Using fictitious bank balances, outstanding invoices and recurring expenses, participants build a thirteen-week forecast and identify the week of greatest liquidity pressure. Transfer to the workplace: Adapt the model to an authorised scope, have the assumptions validated by the finance manager, then arrange weekly updates. Compare forecast and actual receipts to adjust the payment terms assumed.

Objectives of this training

  • Reconcile the opening balance with the available bank data.
  • Convert payment schedules into documented weekly cash flows.
  • Build a model that distinguishes inputs, assumptions, calculations and results.
  • Compare three scenarios and identify where financing is needed.
  • Measure variances between forecasts and actuals.

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Finance managers and treasurers preparing short-term forecasts.
  • Management accountants and accountants responsible for payment schedules and bank account monitoring.

Prior skills

  • Understand cash flows and customer and supplier payment due dates.
  • Be able to use formulas, filters and references in a spreadsheet.
  • Be able to formulate a structured request to an AI assistant.

What you will work on

  1. Define the banking scope

    Objective: Define the forecasting conventions. Rolling horizon, accounts included, available balance and the distinction between profit and liquidity.

  2. Make payment schedules reliable

    Objective: Obtain dated cash flows without duplicates. Outstanding invoices, overdue payments, recurring expenses, VAT-inclusive conventions and treatment of taxes according to the assumptions provided.

  3. Assemble the model

    Objective: Calculate weekly balances. Separation of inputs and formulas; carrying balances forward; AI assistance to suggest explainable formulas.

  4. Simulate timing shifts

    Objective: Assess sensitivity to assumptions. Customer payment delays, supplier payment schedule, exceptional expenditure and the alert threshold defined for the case.

  5. Test consistency

    Objective: Detect errors in the model. Signs, carried-forward balances, omissions and variation tests; limitations of AI-generated suggestions.

  6. Update and present

    Objective: Organise an actionable financial review. Amount and timing variances, rolling updates, version traceability.

What you take away

  • Thirteen-week cash flow model with weekly periods.
  • Register of assumptions, sources and update dates.
  • Comparison of base, cautious and favourable scenarios.
  • Checks worksheet and forecast-to-actual variance table.

Method and assessment

  • Short teaching inputs followed by immediate practice on the running case study.
  • Individual work followed by work in pairs, with peer review of outputs.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Arrange access to a spreadsheet application and an AI assistant authorised by the organisation; exercises use only fictitious or anonymised data. Identify your organisation’s cash flow categories and validation process without transmitting confidential data.

Observable criteria: The opening balance matches the statement provided, with no unexplained difference. ; Each significant cash flow has a source and a date allocation rule. ; Each closing balance equals the opening balance plus net cash flows. ; Scenarios modify identified assumptions without overwriting formulas. ; Variances are quantified and conclusions are submitted for validation by a qualified professional..

Frequently asked questions

Does AI calculate the forecast on its own?

No. It assists with structuring; calculations are performed and checked in the spreadsheet.

Can real bank statements be used?

The exercises use fictitious or anonymised cases, exclusively with authorised tools.

How should an uncertain date be handled?

Document a payment assumption and test a shift in its timing in the cautious scenario.

Does the model decide on payments?

No. No sensitive decisions are automated; a qualified professional validates the conclusions before use.

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