AI in Fraud Detection and Audit: What Works, What Doesn’t, What to Watch

Artificial intelligence has moved from conference slides into everyday audit and compliance work. Used well, it lets small teams cover far more data and spend more time on judgement. Used carelessly, it creates new risks — confidential data leakage, unverified conclusions and over-reliance on tools nobody fully understands.

Where AI genuinely helps today

1. Full-population analytics. Instead of sampling, machine-learning and rules-based analytics can scan every payment, journal entry or expense claim for anomalies: unusual timing, amounts, counterparties or combinations. AI is particularly good at ranking transactions by how unusual they are, so investigators look at the riskiest first.

2. Unstructured data. Contracts, emails, invoices and policy documents can be searched, classified and summarised at a scale no team could read manually — useful in investigations, contract compliance and third-party due diligence.

3. Productivity in professional work. Generative AI can draft audit programmes, test scripts, interview outlines, policy summaries and first drafts of reports. The time saved goes into reviewing and challenging, which is where the value is.

4. Continuous monitoring. Models can watch transactions in near real time and alert on patterns associated with known schemes, such as vendor bank-detail changes followed by large payments.

Where it does not (yet)

• It does not establish facts. An anomaly is a lead, not evidence. Conclusions still require documents, reconciliations and interviews.
• It can be confidently wrong. Large language models may produce plausible but false statements, citations or calculations. Every output used in professional work must be verified.
• It reflects its data. Poor or biased data produces poor or biased alerts — and many false positives that erode trust.

Guardrails every team needs

1. Data protection first. Do not paste confidential or personal data into public tools. Use enterprise solutions with clear contractual protections, and check GDPR implications.
2. Human review and documentation. Record what the tool did, what you checked and the professional judgement you applied.
3. Explainability. You must be able to explain why a transaction was flagged — to management, auditors or a court.
4. Governance. Maintain an inventory of AI tools in use, owners and risk classification. The EU AI Act (Regulation (EU) 2024/1689) introduces obligations that depend on how an AI system is used; organisations should understand where their uses fall.

A sensible way to start

Pick one well-defined problem — for example duplicate or anomalous vendor payments — prove value on historical data, document the method and then scale. Train people at the same time: the skill that matters most is knowing which questions to ask the data, and how to challenge the answer.

Our AI for Audit, Risk & Compliance course is designed exactly for this, and is available as a live in-house session or a self-study package.