Reliable financial reporting

How can you turn monthly data into a traceable financial summary with the help of AI?

1250 € per session, excl. VAT

In short

In a half-day session of 3 hours build a monthly reporting pack combining financial indicators, variance analysis and management commentary. Using a fictional case, use AI to structure and rephrase content, then check the calculations and interpretations before validation by a qualified financial professional.

Why this training?

A monthly reporting pack can present accurate figures while still leading to a misleading interpretation: non-comparable periods, ambiguous sign conventions or causal commentary without evidence. The work therefore focuses as much on data reliability as on interpretation. Generative AI helps organise sections and draft an initial summary, but is neither an accounting source nor financial validation. Participants learn to link each comment to the available data and make uncertainties visible. Running case study: The fictional organisation « Sorélis Vitrage » is preparing its September management review. Its revenue is increasing, but its margin is falling. Participants have fictional extracts of actuals, budget and previous-month data, with a duplicate, a misclassified expense and missing information to address. Application at work: At the next period-end close, reuse the template within an authorised scope, record the corrections requested by the finance manager and measure quality using the same checklist: reconciliations, traceability, consistency of commentary and explicit caveats.

Objectives of this training

  • Define the scope, recipients and indicators for the monthly reporting pack
  • Reconcile source data and document anomalies
  • Calculate variances against budget and the previous month
  • Use AI to draft commentary that distinguishes findings from hypotheses
  • Assess the quality of the reporting pack before financial validation

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Management controllers and financial analysts responsible for monthly reviews
  • Accounting managers and finance staff preparing a summary for management

Prior skills

  • Be able to read a simplified profit and loss statement and calculate a margin and a budget variance.
  • Have prior experience of making simple requests to generative AI and know how to use a spreadsheet.

What you will work on

  1. Scope the management review

    Objective: Define the information required by management. Closed period, scope, recipients; revenue, margin and expenses; sign conventions and comparability.

  2. Ensure reliable figures and variances

    Objective: Build a verifiable numerical basis. Reconciliations, duplicates, expense classification, variances in value and percentage terms; handling missing data.

  3. Write an evidence-based analysis

    Objective: Turn verified variances into useful commentary. An AI prompt specifying the context, permitted figures, format and prohibition on inventing causes; prioritisation of variances.

  4. Check before distribution

    Objective: Measure quality and prepare for validation. Consistency between tables and commentary, rounding, readability, confidentiality, version and validation workflow.

What you take away

  • Scoping sheet and indicator dictionary
  • Monthly table of actuals, budget and variances, with reconciliations
  • Traceable management summary with commentary
  • Completed control checklist and validation requests

Method and assessment

  • Short instructional inputs followed by immediate practice on the running case study.
  • Individual work followed by work in pairs, with cross-review of the outputs.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Arrange access to a spreadsheet and generative AI authorised by the organisation; no unauthorised transfer of confidential data. Bring a blank or anonymised financial summary template if available; the fictional exercise dataset allows you to work without internal documents.

Observable criteria: Each indicator specifies its formula, period, unit and source. ; Totals are reconciled to the extracts; each residual difference is documented. ; Absolute and relative variances are recalculated, with explicit treatment of zero baselines. ; Each comment refers to a checked figure and distinguishes fact, hypothesis and missing information. ; The checklist identifies corrections and points submitted to the qualified financial professional..

Frequently asked questions

Does AI perform the accounting close?

No. The exercise starts from available extracts and replaces neither closing entries nor their verification.

Can we use our actual figures?

The session uses fictional or anonymised data, exclusively within tools authorised by the organisation.

What should you do if an explanation is missing?

Flag the uncertainty and ask the person holding the information to verify it, rather than inventing a cause.

Does the reporting pack enable an automatic decision?

No. It remains subject to validation by a qualified professional; no sensitive decision is automated.

Last updated: