Assisted accounting checks

How can you use AI to identify suspicious entries without confusing a warning signal with an accounting error?

1250 € per session, excl. VAT

In short

In 3 hours you use a fictitious accounting extract to define checks, examine alerts and document the necessary verifications. AI helps formulate hypotheses and structure the reporting; calculations are checked separately. You produce a register of potential anomalies and measure its quality against a model answer.

Why this training?

An unusual amount, a repeated reference or a date discrepancy does not prove that an error has occurred. Recurring purchases, credit notes and adjustments also produce misleading signals: examining them requires accounting context and supporting documents. Generative AI can suggest lines of enquiry and organise findings, but it can also invent a justification or miss an entry. A traceable approach therefore separates detection rules, available evidence and the conclusion validated by a qualified professional. Running case study: The fictitious company Sorellis Outillage is preparing its monthly close. Its purchases journal contains possible duplicates, credit notes, unusual dates and questionable account allocations. An extract of 80 entries, a simplified chart of accounts and fictitious supporting documents enable investigation without assuming that every discrepancy constitutes an error. Application at work: Within a limited, authorised scope, review the checklist with the accounting manager, verify alerts against supporting documents and monitor false positives. Any correction remains subject to validation by a qualified professional, with no automated sensitive decisions.

Objectives of this training

  • Define detection rules suited to the purchases journal
  • Formulate an AI prompt specifying fields, checks and limitations
  • Verify alerts by cross-checking against available supporting documents
  • Classify findings according to their justification and priority for examination
  • Measure false positives and missed anomalies using a case study with a model answer

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Accounts payable accountants and staff responsible for closing checks
  • Accounting managers and financial controllers preparing a review of entries

Prior skills

  • Be able to read an accounting journal and distinguish between debit, credit, invoice and credit note.
  • Have previous experience of using generative AI and know how to filter entries in a spreadsheet.

What you will work on

  1. Prepare the journal and checks

    Objective: Define the scope of the anomalies sought and ensure the extract is reliable. Identifiers, dates, references, signed amounts; missing fields; technical duplicates; confidentiality.

  2. Generate traceable alerts

    Objective: Query AI without delegating the accounting conclusion to it. Contextualised instruction; mandatory references; prohibition on inventing supporting documents; distinction between observation and hypothesis.

  3. Assess discrepancies against supporting documents

    Objective: Distinguish between a probable anomaly, a justified exception and insufficient information. Invoice-to-entry reconciliation; credit notes; dates determining period allocation; consistency of account allocation; priority according to amount and context.

  4. Evaluate detection and prepare the review

    Objective: Measure the limitations of the result before forwarding it. Comparison with the model answer; proportion of confirmed alerts; coverage of known anomalies; human validation.

What you take away

  • Purchases journal checklist
  • Reusable AI instruction with limitations and reporting format
  • Register of alerts linked to entries and supporting documents
  • Detection assessment compared with the model answer and validation workflow

Method and assessment

  • Short instructional sessions followed by immediate practice using 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 the tools authorised by the organisation: an AI assistant and a spreadsheet. Familiarise yourself with internal confidentiality rules; use only the fictitious file provided or effectively anonymised data.

Observable criteria: Each check specifies the fields used and possible exceptions. ; Each alert refers to an identifiable entry and to evidence or a missing supporting document. ; The decisive calculations are reproduced in the spreadsheet. ; The assessment distinguishes between confirmed alerts, false positives and missed anomalies. ; No correction is presented as settled without professional validation..

Frequently asked questions

Does AI confirm fraud?

No. An accounting signal calls for investigation; it demonstrates neither fraud nor intent.

Can you use a real data export?

Only after effective anonymisation and internal authorisation. The fictitious case study is sufficient for all exercises.

Are entries corrected automatically?

No. The deliverable proposes verifications; any correction requires validation by a qualified professional.

How can you check quality without a model answer in a business setting?

Have the alerts and a sample of unflagged entries reviewed; this review does not prove the complete absence of anomalies.

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