AI USE CASE
AI-Augmented Audit Transaction Sampling
ML selects the highest-risk transactions for audit testing, improving coverage and efficiency.
What it is
By training machine learning models on historical transaction data and known audit exceptions, auditors can focus testing on the 5–15% of transactions that carry disproportionate risk, rather than relying on random or judgement-based sampling. This typically reduces audit fieldwork hours by 20–35% while increasing exception detection rates by 30–50%. The approach is particularly effective for large transaction populations in assurance engagements, where manual review of every item is impractical.
Data you need
Historical transaction records with labels or flags indicating past audit exceptions, anomalies, or fraud findings, covering at least two to three audit cycles.
Required systems
- erp
- data warehouse
Why it works
- Involve experienced audit managers to validate model risk features and ensure outputs align with professional judgement.
- Start with a single high-volume client engagement as a pilot before rolling out across the portfolio.
- Maintain explainability by using interpretable models or SHAP values so auditors can justify sampling decisions.
- Establish a feedback loop where audit findings from each cycle are fed back to retrain and improve the model.
How this goes wrong
- Insufficient labelled historical exceptions makes model training unreliable and leads to poor risk stratification.
- Auditors distrust the model outputs and revert to traditional sampling, negating efficiency gains.
- Model trained on past exceptions misses novel or emerging fraud patterns not represented in historical data.
- Client ERP data is inconsistent or poorly structured, requiring excessive cleaning that delays deployment.
When NOT to do this
Do not deploy this if your client transaction data is fragmented across multiple incompatible legacy systems with no centralised data layer — the integration cost will exceed the audit efficiency gains.
Vendors to consider
Sources
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