Managing payment fraud is nothing new for finance teams. While the threat is familiar, the methods behind it have changed dramatically, and that requires a new approach. 

Generative AI now allows bad actors to produce invoices, emails, and voice requests that are nearly indistinguishable from legitimate ones. The volume and sophistication of attacks are rising simultaneously. The finance teams on the receiving end are largely underprepared for what’s coming.

The Yooz 2026 Payment Fraud Readiness Report, which surveyed 750 U.S. finance, accounting, and accounts payable professionals, found that 70% of finance professionals say their organization experienced a payment fraud attempt in the past two years or couldn’t rule one out. Of the organizations that experienced a known fraud attempt, 28% lost money, and 39% of those losses exceeded $50,000.

The threat that has crossed from occasional risk into routine exposure. If fraud attempts are an inevitability, then finance teams need to be ready to catch them before a payment is made.

AI Fraud Is Moving Faster than Finance Team Readiness

Fraudsters using AI can generate invoices that match a vendor’s formatting and branding precisely. They can produce emails that replicate an executive’s tone and communication style. They can create voice requests that sound like the CFO asking an AP team member to push a payment through urgently.

Business email compromise, vendor impersonation, and fake invoice schemes powered by AI are already active, and they’re targeting finance operations regardless of industry and organization size.

Only 21% of finance professionals surveyed by Yooz say they believe their organization is extremely prepared to detect and respond to AI-enabled fraud. That means nearly four out of five finance teams are operating with significant uncertainty about their ability to handle one of the fastest-growing threat vectors in B2B payments.

There’s a cost to that uncertainty. Organizations that experienced fraud attempts, that they weren’t prepared to catch, lost significant amounts of money, with losses frequently reaching six figures.

Why Traditional Fraud Defenses Are Struggling

The fraud controls most organizations rely on were built for a different threat environment. Training employees to recognize suspicious emails worked when suspicious emails had obvious tells, such as wrong domains or poor grammar. AI-generated fraud communications are more sophisticated and often lack those signals. They look right, sound right, and arrive through channels that appear legitimate.

The same applies to invoice verification processes that depend on visual review. A finance professional reviewing an invoice that has been precisely generated to match a known vendor’s template has very little to work with if the verification process relies primarily on appearances. The document looks authentic because it was engineered to.

Manual AP workflows make the problem worse by concentrating fraud risk at the points where human attention is most stretched. The Yooz survey revealed that finance teams with mostly manual AP processes lost money in 42% of known fraud attempts, compared with 30% among highly automated teams and 22% among teams using a combination of automated and manual processes.

Manual workflows require finance teams to review high transaction volumes under time pressure. Verification steps are often inconsistent verification steps, and real-time visibility into payment activity is limited. These are the conditions that fraudsters take advantage of. When every invoice requires human judgment to clear, fraud can succeed simply by looking normal enough to pass.

AI-Powered Detection as a Countermeasure

The same technology powering fraud attacks can power defenses against them, and the organizations moving in that direction are seeing better outcomes.

AI-enabled fraud detection doesn’t rely on humans recognizing that something looks wrong. It screens transactions against historical patterns, flags statistical anomalies, analyzes document metadata for signs of manipulation, and cross-references vendor data against records established through formal onboarding processes. Every transaction is screened the same way, at a scale and consistency no manual review process can match.

This level of scrutiny is needed for the fraud vectors that are growing fastest. Fake invoices engineered to match legitimate vendors can be caught by systems that compare document-level characteristics against prior invoices from that vendor. Business email compromise attempts that request banking changes can be blocked by workflows that require formal verification independent of the email requesting the change. Duplicate payment attempts are caught automatically when every invoice is matched against payment records in real time.

Finance teams that have extended AI into fraud-specific workflows gain a continuous monitoring capability that many aren’t taking advantage of. The Yooz 2026 AI in Finance Reportfound that only 19% of finance teams currently use AI for audit, risk, compliance, or fraud detection and prevention. For organizations that haven’t yet made that move, there’s a major opportunity to reduce their current exposure with AI-powered detection.

What Finance Leaders Should Be Doing Now

The data from the Yooz fraud readiness survey outlines a clear set of priorities for finance teams that want to reduce their exposure.

Formalizing vendor change management is one of the highest-impact steps available regardless of where an organization sits on the automation spectrum. Account number changes, banking updates, and new vendor onboarding should flow through a controlled, system-based process that requires verification through established contact methods, not through the email or phone call requesting the change.

Centralizing invoice intake through a single, auditable channel removes the scattered email-based submission processes that create blind spots in AP operations. When every invoice enters through the same pathway, screening and matching can be applied consistently.

Extending AI into payment workflows gives finance teams the detection capability that manual processes simply can’t provide at scale. Organizations that have automated reporting and forecasting but haven’t yet brought AI into transaction screening and fraud detection are leaving the highest-value application untouched.

As Laurent Charpentier, CEO of Yooz, noted in releasing the survey findings: “Fraud attempts will continue to become more sophisticated, but stronger processes can prevent more fraudulent payments from going through.” The data shows that the solution is to treat fraud prevention as a structural feature of AP operations instead of a training program layered on top of manual workflows.

Finance teams that want to understand where they stand against the current fraud landscape can explore the full findings in the Yooz 2026 Payment Fraud Readiness Report.