Using AI to Detect Payment Fraud and Anomalies in Treasury

Payment fraud remains one of the most damaging operational risks a treasury faces, and the methods used against corporates continue to grow more sophisticated. Business email compromise, invoice redirection, and social-engineering attacks are designed specifically to slip past static, rules-based controls. Artificial intelligence offers a genuinely useful additional layer of defence — not as a replacement for sound process and segregation of duties, but as a way to catch what rules alone miss. This article looks at how anomaly detection works in practice and how to deploy it well.

Why rules alone are no longer enough

Traditional payment controls rely on fixed rules: approval thresholds, whitelisted beneficiaries, dual authorisation above a set value. These remain essential, but they share a weakness — they only catch what someone anticipated and codified in advance. A fraudulent payment engineered to sit just below an approval threshold, or routed to a plausible-looking new beneficiary, can pass every rule and still be fraudulent. Rules are also static in a world where both legitimate business and fraud techniques are constantly changing.

How AI-based anomaly detection works

Rather than checking a payment against a fixed list of conditions, machine-learning models learn what normal payment behaviour looks like for an organisation and flag transactions that deviate from it. In treasury, the patterns worth learning typically include:

• Beneficiaries that are new, rarely used, or recently amended in the master data

• Payment amounts that are unusual for a given supplier, entity, or payment type

• Timing that falls outside normal patterns, such as out-of-hours or period-end anomalies

• Sudden changes to standing bank details on an established beneficiary

• Combinations of factors that are individually unremarkable but collectively suspicious

The output is not an automatic block but a risk score that directs human attention to the payments most worth a second look — before they leave the building.

The fastest treasury AI win

Anomaly detection is often the quickest AI use case to deliver tangible value in treasury, for a simple reason: the cost of a single prevented fraudulent payment can dwarf the cost of the tooling. Unlike forecasting, it does not depend on long, clean histories to be useful, and it complements rather than replaces the controls already in place. For many treasuries it is the most sensible first step into practical AI.

Managing false positives

The main practical challenge is calibration. A model tuned too aggressively floods the team with alerts and quickly gets ignored; tuned too loosely, it misses the events that matter. Getting this balance right requires a feedback loop — analysts confirming which flags were genuine and which were noise, so the model improves over time. It also requires a clear operational process for what happens when a payment is flagged: who reviews it, within what timeframe, and with what authority to hold or release.

Controls, not a silver bullet

AI anomaly detection should sit alongside, never instead of, the fundamentals: strict segregation of duties, robust beneficiary onboarding and change controls, callback verification for bank-detail changes, and staff awareness of social-engineering tactics. The technology is a powerful additional net, but the strongest defences remain layered. Treasuries that bolt AI onto weak underlying controls buy themselves false comfort.

How RG Treasury can help

RG Treasury provides senior, independent treasury consultants — the calibre you would expect from a Big 4 practice, at a fraction of the day rate. Whether you are scoping an AI use case, cleaning up the data behind it, or selecting a new treasury system, we can help you separate what delivers value from what simply sounds impressive. Get in touch to discuss how we can support your team.