AI isn’t just reshaping workplace fraud; it’s accelerating it. Employees can now generate convincing fake receipts in seconds and manipulate documents so subtly that traditional checks rarely stand a chance. At the same time, employers are turning to AI to detect patterns and anomalies that conventional checks can miss, creating an escalating contest between AI-enabled deception and AI-powered detection.

Workplace Fraud On The Rise

AI hasn’t invented workplace fraud, but it is removing much of the friction involved. Expense fraud is one example. Emburse research shows that 40% of U.S. employees surveyed used AI to generate a fake receipt, including 19% who fabricated a purchase entirely and 15% who inflated the value of a genuine purchase.

Detection data suggests the technique is spreading rapidly. AppZen reported that AI-generated receipts accounted for 70.8% of the fraudulent receipts flagged by its customers in mid-May 2026, up from zero in March 2025. Generative AI can also create convincing invoices, payslips, bank statements and other supporting documents in seconds. The fraud itself may be familiar; what has changed is how quickly, cheaply and convincingly employees can manufacture the evidence needed to support it.

As fraud techniques evolve, employers are under pressure to deploy AI tools that keep them one step ahead. But the same technologies that promise sharper detection also raise questions about privacy, legality and the limits of automation. The emerging challenge is not simply catching fraud, but doing it responsibly.

AI That Connects the Dots

Peter Barnett, VP of product strategy at Action1, argues that AI’s biggest advantage is its ability to correlate seemingly ordinary behaviors into meaningful risk signals. Rather than flagging isolated anomalies, modern systems look for patterns.

“For example, an employee suddenly installing unauthorized software, accessing sensitive systems outside their normal hours, and then transferring a large amount of data would deserve more attention than any one of those actions alone,” he says.

Another example might be unusual payroll or expense activity combined with changes in account access. “If someone starts submitting unusual expenses while also accessing financial systems they normally do not use, the combined pattern becomes more meaningful,” says Barnett.

Crucially, he says, AI should compare employees against their own historical behavior rather than a universal benchmark. “People have different working patterns, so AI should learn what is normal for a particular role or device and flag significant deviations. This creates better signals while reducing unnecessary alerts.”

Balancing Automation With Human Judgment

For small businesses, there is a risk of over-reliance on automated anomaly detection. Bogdan Condurache, cofounder and CPO of Brizy.io , warns that fraud detection systems must remain tightly scoped to business risks, not employee surveillance.

“There is a big difference between checking whether expenses match company rules and constantly tracking someone’s activity on their computer,” he says. “AI should handle repetitive work, such as checking hundreds of transactions and identifying unusual patterns. Humans should handle the context. An employee may have a perfectly valid reason for an unusual expense or an unusual working pattern that the AI cannot understand.”

His advice echoes a broader trend. AI should surface anomalies, but humans must interpret them.

When AI Spots What People Miss

Direction.com manages SEO for pharmacies, but also oversees inventory and loss‑prevention systems. CEO Chris Kirksey recalls one incident in a pharmacy that fills around 400 prescriptions a day and processes dozens of refunds weekly.

“Most refunds come from customers who change their minds or receive the wrong item,” he says. “We installed a system called TecsysIQ Image Capture to track high-value inventory like brand-name drugs and expensive supplements, because we kept finding unexplained gaps between the system count and the physical shelf.”

Three months in, the system flagged a pattern: one staff member was processing 40% more refunds than colleagues. The AI cross-checked each refund against image logs. In every case, the employee scanned the refund barcode, but the camera never captured the product being placed in the return bin.

“I reviewed 27 refunds over six weeks,” says Kirksey. “Each one followed the same script: a quick refund, no product returned, and the cash drawer balanced because the refund was processed as a store credit. The employee pocketed the cash from those credits. Total loss was $2,400. We called the person in, showed them the image logs, and they admitted everything. Termination was immediate.”

Without those image logs, says Kirksey, the losses would likely have been attributed to supplier errors or customer theft. He adds: “From that incident, we now run the same setup across all three locations and have not seen another incident. We are also piloting an Inventory Visibility module to track stock levels in real time, so we can catch problems before they become losses.”

Catching Fraud Before Approval

Document manipulation is another fast‑growing fraud vector. Tools like Copyleaks can pinpoint manipulated text or image regions, a level of granularity that changes how employers investigate expense fraud or document‑based deception.

CEO Alon Yamin says the key is seamless integration. “Take an invoice landing in an accounts-payable inbox, for example,” he says. “Instead of a human reviewer clearing it and only catching a problem after payment goes out, the tech can flag it at intake, before it ever reaches the approval queue, and point to the specific object that looks altered.”

This could be a total that doesn’t match the line items or a signature that doesn’t match past invoices. “The reviewer isn’t handed a black-box risk score. Instead, they see a flagged region so they can approve, escalate, or ask for the original document with context, instead of guessing,” he adds.

Detection Without Surveillance

Employers adopting AI‑enabled fraud tools must ensure that technological capability does not dilute employment rights. Katie Maguire, partner at Devonshires , says the boundary between legitimate monitoring and unlawful surveillance is defined by three principles: necessity, proportionality and transparency.

“Primarily, monitoring must be justified with a legitimate business purpose,” she says. “Fraud prevention is a valid aim, but employers must be able to justify why less intrusive methods are insufficient, and why AI monitoring is therefore needed.”

Even in high‑risk roles, monitoring must remain targeted. And under GDPR and the Data Protection Act, employees must be informed.

When AI does flag suspicious behavior, Maguire stresses that alerts are indicators, not evidence. “In employment disputes, tribunals expect employers to rely on verified, explainable and corroborated evidence, not just algorithmic suspicion,” she says. “AI is good as a supporting tool, but it is not a replacement for human judgment, and AI-flagged anomalies should be supported by evidence like system logs, receipts or CCTV footage. A single AI alert is not enough to justify disciplinary action.”

AI Is Reshaping Fraud, But It Can’t Replace Judgment

AI is transforming workplace fraud on both sides of the equation. It is making deception easier to generate, but also giving employers an opportunity to turn machine-detected anomalies into fair, explainable and corroborated decisions. In the AI-driven fight against workplace fraud, spotting what looks wrong is becoming easier. Knowing what it means still requires a human.