AI in Finance
AI for Anomaly Detection in Financial Controls
16 March 2026
Anomaly detection and fraud detection are not the same thing. This distinction matters because most finance teams, when they hear “AI anomaly detection,” think primarily about fraud. The fraud use case is real. It is also not where most of the value sits for a typical finance function.
Fraud is relatively rare. Errors, inconsistencies, and control failures are not. They happen continuously, at scale, and in most finance functions they go undetected because the volume of transactions makes manual review statistically impossible. That is where AI anomaly detection does its most consistent work.
Understanding what the tool actually does, what it requires, and how to govern it properly will determine whether it becomes a genuine controls asset or another expensive underperformance.
What AI anomaly detection actually does
The core mechanism is plain. The system learns the normal pattern of your transactions over time, then flags deviations from that pattern. Not all deviations are problems. But deviations are where problems hide.
For a finance function, this means the system watches for patterns like these. Duplicate payments: same supplier, same amount, similar dates, possibly different invoice references. Round-number clustering: an unusual concentration of transactions at round amounts, which can indicate either estimation practices that should concern you or something more deliberate. Supplier master changes immediately before payment runs: a vendor added or reactivated in the 48 hours before a payment cycle is a pattern that warrants human attention. Journal entries posted outside normal working hours by users whose profile does not include routine journal activity: not necessarily fraud, but not nothing either. Expense claims that pattern differently from a person’s historical submissions: a sudden change in submission frequency, category mix, or amount profile.
None of these flags mean something is wrong. All of them mean something is worth looking at.
The numbers are more compelling in practice than they sound in principle. In a well-implemented AP anomaly detection system, duplicate payment detection alone typically recovers 0.5 to 1.5% of invoice value in the first year. That is money that was already out the door. For a finance function processing £10 million in payables annually, that recovery range is £50,000 to £150,000. It was not expected. It was not budgeted. It comes from payments that cleared, suppliers who received double payments and did not proactively flag them, and a process that was doing everything right by its own internal logic while still producing the error.
This is why the distinction from fraud detection matters. Fraud is intentional and often sophisticated. The duplicate payment problem is almost always a process failure. No one is doing it on purpose. The volume makes it invisible to manual review. AI makes it visible because AI reviews the population, not a sample.
The controls layer
Continuous controls monitoring is the framing that makes the most sense for anomaly detection in a finance context. Not a one-time implementation, not just an audit tool. A permanent layer in the control environment that runs in parallel with everything else.
Manual controls testing covers samples. Depending on your audit approach, your external auditors are testing a fraction of transactions. Your internal audit team, if you have one, is doing the same. This is not a failure of methodology. Sampling is statistically valid and practically necessary. But it has a structural limitation: the transactions not in the sample are not tested.
AI anomaly detection covers populations. Every invoice. Every journal entry. Every expense claim. Every bank transaction. This is a different kind of coverage, and the difference matters for a finance function trying to maintain a strong control environment.
The practical implication is that anomaly detection changes what controls assurance actually looks like. Instead of “we tested a sample and found nothing material,” you can say “we reviewed 100% of transactions and investigated the flagged items.” That is a more defensible position. It also produces more reliable information for management.
This is not a replacement for manual controls. It is a layer that operates at a scale manual controls cannot reach, referring issues to human judgment rather than replacing it.
What it requires
Anomaly detection is not plug-and-play. Three things determine whether it works.
First: clean, consistent transaction data. The pattern recognition is only as good as the pattern. If your transaction data is noisy, inconsistently coded, or structured differently across entities or time periods, the system learns noise. Noisy data produces noisy anomaly flags. A high false-positive rate means the alerts get ignored. Ignored alerts mean you have spent money on infrastructure that is not functioning as a controls tool.
This connects directly to the data quality work that underpins all AI in finance. If your data foundations are not in order, anomaly detection does not solve a controls problem. It reveals a data problem. Both outcomes are useful information, but only the second one costs you the implementation budget. See data quality in AI finance for the full detail on what clean data actually requires.
Second: defined thresholds for alerting. The system needs configuration, not just data. Which deviations are significant enough to flag? What is the threshold for a “round number” anomaly? How similar does a duplicate invoice need to be before it gets flagged? These decisions require someone who understands both the tool and the specific characteristics of your transaction patterns. Initial configuration is usually done by the vendor’s implementation team. It should be reviewed and refined based on real-world results, not treated as a one-time setup task.
Third: a human review process that is actually used. A system that generates 200 alerts a week and has them reviewed in a bulk session at month-end is not functioning as a controls tool. By the time the review happens, the transaction has long since settled. The only value being extracted is retrospective learning, not real-time control.
Alert review needs to be built into normal operating rhythm. Depending on the alert volume and your team’s capacity, this might mean a daily or weekly review by a specific owner with authority to investigate and escalate. It is not glamorous. It is the difference between a controls tool and controls theatre.
The governance point
Anomaly detection generates false positives. This is not a flaw. It is an inherent property of any system that identifies deviations from a pattern. Some deviations are legitimate. A round-number payment might be a correctly negotiated contract amount. A supplier added before a payment run might be a legitimate new vendor brought on urgently for a project.
The ratio of genuine issues to false positives determines whether the tool is useful or whether it produces noise. A system with a 90% false-positive rate means reviewing nine legitimate transactions for every one that has a problem. That is still valuable if the one genuine issue is material. It is not sustainable as a daily operational process at high alert volumes.
This requires tuning over time. The initial configuration will not be optimal. It should improve as the system accumulates more data and as the humans reviewing alerts provide feedback. That feedback loop is essential. If reviewers are not recording their findings, if there is no mechanism to tell the system “this type of alert is consistently a false positive in our context,” the tuning cannot happen and performance will not improve.
Governance design for anomaly detection should specify: who owns the review process, what the escalation path is when a genuine issue is identified, how findings are recorded, and how the feedback loop from reviewers to the model operates. This is not optional. A well-implemented anomaly detection tool with poor governance will underperform a less sophisticated tool with disciplined governance. The technology is the easy part.
See AI governance for finance for the broader framework within which anomaly detection governance should sit.
Where to start
If you are considering anomaly detection and do not know where to begin, start with duplicate payment detection. It is the most practical entry point for three reasons.
Precision is high. Duplicate payment detection has a lower false-positive rate than more complex pattern recognition, because the matching logic (same supplier, similar amount, similar date) is relatively well-defined. You will not spend your first weeks processing alerts that go nowhere.
ROI is visible. Recovery of duplicate payments produces a direct, quantifiable return in year one. This is useful for the internal business case and useful for demonstrating to sceptical stakeholders that the tool is delivering value. The numbers are real, they are yours, and they are not dependent on projections about future efficiency gains.
Data requirements are manageable. Duplicate payment detection works on your AP transaction data. You do not need to integrate data from multiple source systems, resolve data quality issues across complex entity structures, or build a unified data model before you can start. For a single-entity finance function with a reasonably maintained AP ledger, this is often deployable without the data preparation project that more complex anomaly detection requires.
Once duplicate payment detection is running well and your team has developed the habit of reviewing and acting on alerts, the more complex pattern recognition becomes a natural extension rather than a first step.
The broader question of whether your finance function is ready for AI-driven controls sits within a larger readiness picture. The AI readiness assessment is the place to work through that accurately before making implementation decisions.
Maebh Collins is a Fellow Chartered Accountant (FCA, ICAEW) with Big 4 training and twenty years of operational experience as a founder and senior finance leader. She writes about AI in finance transformation from the inside out.