Financial anomaly review
How can you organise a traceable, testable and supervised review of financial anomalies using generative AI?
3500 € per session, excl. VAT
In short
In two days, totalling 14 hours excluding breaks, design a prototype for reviewing financial anomalies: flagging rules, an evidence file, an assessment workflow and a quality dashboard. Generative AI supports document analysis and synthesis; calculations are checked separately and conclusions are validated by a qualified professional.
Why this training?
A list of atypical accounting entries does not constitute a review: without evidence, an owner or a status, flagged items accumulate and the same exceptions recur at every close. Generative AI can structure supporting documents and prepare investigation questions. Separating flagging, interpretation and validation limits unfounded conclusions and makes quality measurable. Running case study: The fictitious company « Pernoval Composants » is preparing its month-end close. A supplier data export and fictitious supporting documents contain apparent duplicates, discrepancies in amounts and unreconciled credit notes. The team must distinguish between confirmed anomalies, justified exceptions and incomplete files. Workplace application: Propose a pilot limited to one supplier cycle, under the responsibility of the finance function: an authorised sample, a review running alongside the existing process, weekly measurement of errors and a human decision on whether to continue.
Objectives of this training
- Define a typology of anomalies and the evidence expected for each.
- Develop reproducible flagging rules.
- Prototype an AI-assisted assessment workflow.
- Measure false positives, missed anomalies and the quality of justifications.
- Formalise the responsibilities and conditions for the pilot.
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Accounting managers and financial controllers organising period-end close reviews.
- Internal auditors and finance data leads preparing a supervised pilot.
Prior skills
- Be proficient in supplier reconciliations and closing controls.
- Be able to filter an export and check calculations in a spreadsheet.
- Have experience using structured prompts with a generative AI assistant.
What you will work on
Define the scope of the review
Objective: Define the supplier scope. Anomaly, exception and suspicion; materiality and exclusions.
Prepare the evidence
Objective: Make the sources reconcilable. Technical identifiers, missing fields, anonymisation and access.
Specify the flagging criteria
Objective: Establish verifiable rules. Duplicates, discrepancies, credit notes; documented thresholds and exceptions.
Set boundaries for generative assistance
Objective: Produce source-referenced syntheses. Prescribed format, references, abstention from answering; supporting documents treated as data, not as instructions.
Build the register
Objective: Ensure human handling is traceable. Statuses, owner, deadline, evidence and escalation.
Build the reference set
Objective: Prepare an independent evaluation. Domain-expert annotation, disagreements and separation of tuning and testing.
Carry out the review
Objective: Test the complete workflow. Assessment, document requests and validation.
Measure the errors
Objective: Quantify the observed quality. Precision, recall, false positives, missed anomalies and sample limitations.
Correct and retest
Objective: Reduce the identified defects. Rule errors, missing evidence and fabricated interpretations.
Prepare the pilot
Objective: Decide on the conditions for experimentation. Review workload, acceptance thresholds, stopping and reverting to the existing process.
What you take away
- Matrix of anomalies, rules, evidence and responsibilities.
- Prototype review register and version-controlled AI prompts.
- Annotated test set and quality dashboard.
- Pilot plan with stopping conditions and human validation.
Method and assessment
- Short instructional inputs followed by immediate practice on the case study used throughout.
- Individual work followed by work in pairs, with peer review of the outputs.
- Facilitated group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Describe an existing financial control, its pain points and its escalation route. Check which tools the organisation authorises; plan to use only fictitious or anonymised data.
Observable criteria: Each flag refers back to the source entries and a documented rule. ; Calculations are reproduced independently of the generated responses. ; Conclusions distinguish between evidence, hypotheses and missing information. ; Precision and recall are calculated on a separate annotated batch. ; Every case closure records a qualified approver and a justification..
Frequently asked questions
Does the system block payments?
No: no sensitive decisions are automated; the qualified professional retains decision-making authority.
Can real accounting entries be used?
Only anonymised and authorised entries; the applicable requirements must be checked with your legal department.
Does software need to be developed?
No, a register and an authorised assistant are sufficient for the training prototype.
Is this a production deployment?
No, the two days produce a tested prototype and a pilot plan.
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