Improve the reliability of financial data
How can you prototype traceable financial checks and measure the anomalies detected without increasing false alerts?
3500 € per session, excl. VAT
In short
Two days, equivalent to 14 hours excluding breaks, to turn financial data risks into testable controls. Using an approved generative AI tool, participants specify rules, build a prototype and evaluate its results against known anomalies. They prepare a supervised pilot, without aiming for immediate deployment at scale.
Why this training?
A plausible amount can conceal a missing currency, a duplicate invoice or an incorrect allocation to a period. Isolated controls are not enough: their scope, exceptions and owner must be explicit. Generative AI helps formulate rules and suggest scripts, but may invent columns or misinterpret an accounting convention. Each proposal is checked against the data dictionary, executed and reviewed by a qualified finance professional. Running case study: The fictitious company « Terliane Connectique » is preparing its monthly close. Its supplier invoice files, accounting entries and third-party master data contain duplicates, missing currencies and period mismatches. The prototype must distinguish confirmed anomalies from legitimate business exceptions. Transfer to the workplace: Propose a pilot within a limited financial scope, with a designated owner, prior validation of the rules, monitoring of alerts and a review comparing detection, review workload and residual anomalies.
Objectives of this training
- Mapping quality risks across a financial flow
- Specifying rules with thresholds, exceptions and owners
- Prototyping reproducible controls with AI assistance
- Measuring precision, recall and risk coverage
- Defining the conditions for launching and stopping a pilot
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Accounting and management control managers overseeing the reliability of financial closes
- Financial analysts and data quality managers designing checks
Prior skills
- Understand the flows of invoices, accounting entries and third-party master data.
- Be able to manipulate tabular data and read a simple formula or query.
- Already use generative AI and verify its responses.
What you will work on
Prioritising closing risks
Objective: Define the scope of the controls. Completeness, uniqueness, consistency, timeliness; financial materiality.
Assessing the source files
Objective: Establish data interpretation conventions. Keys, granularity, currencies, dates and null values.
Specifying the rules
Objective: Make the controls unambiguous. Duplicates based on composite keys, orphan references, consistency of amounts.
Building the test environment
Objective: Establish expected results. Compliant cases, injected anomalies, boundary cases and legitimate exceptions.
Assembling the prototype
Objective: Run the first controls. Formulas or scripts depending on the context; version traceability.
Ensuring reliable execution
Objective: Prevent results from being incomplete without warning. Missing columns, unexpected formats, join errors.
Measuring detection
Objective: Quantify performance. True positives, false positives, false negatives; precision and recall.
Adjusting without concealing
Objective: Reduce unjustified alerts. Thresholds, tolerances and documented exceptions.
Organising the financial review
Objective: Assign responsibility for handling alerts. Evidence, severity, escalation and closure of anomalies.
Defending the pilot proposal
Objective: Prepare a controlled experiment. Scope, frequency, access, acceptance criteria and stopping criteria.
What you take away
- Risks–data–controls matrix
- Version-controlled catalogue of rules and exceptions
- Executable prototype and results log
- Test report with a confusion matrix
- Pilot plan and alert review procedure
Method and assessment
- Short instructional sessions followed by immediate practice on the continuing case study.
- Individual work followed by work in pairs, with peer review of the outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Prepare a fictitious or anonymised data dictionary of financial fields and a few business rules. Check which tools the organisation permits and prepare an isolated execution environment.
Observable criteria: Each priority risk is linked to a rule and an owner. ; Each rule specifies fields, handling of null values, the threshold and exceptions. ; The prototype reproduces the same results on the same file. ; Precision and recall are recalculated from the labelled anomalies. ; The pilot provides for validation by a qualified person, monitoring and stopping criteria..
Frequently asked questions
Can we use our accounting files?
Only fictitious or anonymised cases, using tools authorised by the organisation.
Does the AI decide on corrections?
No. No sensitive decisions are automated; corrections remain subject to validation by a qualified person.
Is programming required?
Not necessarily: a formula-based prototype is suitable, provided the checks remain reproducible and verifiable.
Will the solution be operationalised at scale?
No. The two days produce a tested prototype and a pilot plan, not a production deployment.
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