No. AI is very good at spotting certain kinds of fraud in accounts payable and genuinely weak against others. Any vendor telling you their AI catches everything is telling you something about their marketing, not their technology.
That answer deserves a proper explanation, because the difference between what AI detects reliably and what slips past it should shape how you design your AP controls. Let’s take an honest look at both sides.
Why AP is where fraud concentrates
Accounts payable is the point where money leaves your company by design, which makes it the natural target. The 2026 AFP Payments Fraud and Control Survey found that 76% of US organizations experienced attempted or actual payments fraud in 2025, and 74% were hit by business email compromise, where attackers impersonate executives or suppliers to redirect legitimate payments.
The same survey contains a striking gap: only 17% of organizations currently use AI to combat payments fraud. Those that do report faster fraud reporting, better detection of deepfakes, and real-time identification of suspicious activity. So the technology helps. The question is where.
Where AI genuinely improves fraud detection in AP
AI’s advantage is scale and memory. A human reviewer sees one invoice at a time. A model sees every invoice, every vendor, and every payment your company has ever processed, and it never gets tired on the last Friday of the quarter. That makes it strong against fraud that leaves statistical fingerprints:
Duplicate and manipulated invoices
Fraudsters often resubmit a real invoice with a new number, a changed date, or a slightly altered amount. Exact-match checks miss these variations. AI models that compare invoices across many attributes catch near-duplicates that would sail past a rules engine, and they do it at the point of invoice capture, before the document enters your workflow.
Deviations from vendor behavior
Every supplier has a rhythm: typical amounts, typical frequency, typical bank accounts, typical line items. When an invoice arrives that breaks the pattern, an unusually round amount, a first-ever urgent payment request, a new bank account in a new country, anomaly detection flags it in real time. This is how AI catches invoice redirection attempts even when the email that started them looked flawless.
Ghost vendors and collusion signals
Cross-referencing vendor master data against employee records surfaces the classic internal scheme: a vendor whose address, bank account, or tax ID matches an employee’s. Humans rarely run these checks systematically. Software does it on every record, every day.
Policy circumvention
Invoices split into smaller amounts to stay under approval thresholds form a recognizable pattern when you analyze them together. Individually, each one looks compliant.
Where AI falls short
Now the uncomfortable part.
First-time fraud with clean paperwork
Machine learning detects deviations from historical patterns. A fraudulent vendor that’s onboarded properly, invoices plausible amounts at plausible intervals, and matches its purchase orders generates no anomaly, because there’s no honest history to deviate from. Well-designed procurement fraud can look statistically perfect.
The human endpoint
Business email compromise ultimately targets a person, not a system. AI can flag the resulting payment instruction as unusual, and increasingly can spot AI-generated phishing text, but if an authorized approver is convinced the CFO wants a payment made, they may override every warning the system raises. The AFP data shows deepfake voice and video impersonation is making this problem harder, not easier.
Collusion between insiders
When the person committing fraud is also the person approving it, or two colleagues split the roles, the transaction follows the approved workflow. Detection then depends on structural controls like segregation of duties and vendor verification, not pattern analysis.
Whatever the model hasn’t seen
Models learn from historical fraud. A genuinely novel scheme has no training data. This is why detection rates are high for known fraud types and unavoidably lower for new ones.
Common mistakes when implementing AI in accounts payable
Understanding the limits above helps you avoid the mistakes we see most often:
- Treating AI as a replacement for controls rather than a layer on top of them. Segregation of duties, callback verification for bank detail changes, and clean vendor master data still do the heavy lifting. AI amplifies good controls; it can’t compensate for missing ones.
- Ignoring data quality. Models trained on messy, inconsistent AP data produce noisy alerts, and teams quickly learn to ignore noisy alerts. If your invoice data lives in three formats across four systems, fix that first. Our recent practical guide to cloud-based AP covers what a clean, centralized invoice process looks like.
- Tuning for zero friction. If every flagged invoice creates work, there’s pressure to loosen thresholds until nothing gets flagged. The better path is routing exceptions into an efficient review workflow so the cost of investigating stays low.
- No human accountability for overrides. Someone will always be able to push a payment through. Record who, when, and why, so overrides are visible and auditable rather than silent.
What this means for your AP strategy
The right conclusion isn’t skepticism about AI. It’s precision about the job you’re hiring it for. AI-supported fraud detection meaningfully reduces losses from duplicate invoices, manipulated documents, redirected payments, and anomalous vendor activity, which together account for a large share of real-world AP fraud. Structural controls and verification processes cover the rest. Neither works well alone.
This layered approach works best when detection is built into the process rather than bolted on afterward. Serrala’s AP automation applies AI checks during capture, validation, and approval routing, so suspicious invoices are held before posting rather than investigated after payment. Forrester recently named Serrala a Strong Performer in its Q2 2026 Wave for AP invoice automation, noting the tight linking of invoice, PO, and payment data that makes this kind of cross-checking possible. And because our AI capabilities run across AP, AR, and payments together, a bank account flagged in one process is flagged in all of them.
Fraudsters are already using AI. The AFP numbers suggest most finance teams aren’t yet using it back. Closing that gap won’t make you invulnerable, but it will make you a much harder target than the company next door, and in fraud prevention, that counts for a lot.
Frequently asked questions
How does AI improve fraud detection in accounts payable?
AI reviews every invoice, vendor, and payment together rather than one at a time, which lets it catch fraud that leaves statistical traces: near-duplicate invoices, deviations from a supplier’s normal behavior, new bank accounts on established vendors, vendor records that match employee details, and invoices split to stay under approval thresholds. It runs these checks continuously and at the point of capture, so suspicious items are flagged before payment rather than found in an audit afterward.
Can AI replace manual controls in AP?
No. AI works best as a layer on top of structural controls, not a replacement for them. Segregation of duties, callback verification for bank detail changes, and clean vendor master data still do the foundational work. AI amplifies good controls and struggles to compensate for missing ones, which is why the strongest AP fraud defenses combine both.
What types of fraud does AI struggle to detect?
Three main categories. First, well-executed first-time fraud with clean paperwork, because there is no honest history to deviate from. Second, business email compromise that convinces an authorized approver, since the weak point is a person overriding the system. Third, collusion between insiders, where the transaction follows the approved workflow. Novel schemes with no precedent in the training data are also harder to catch by definition.
How many companies use AI to fight payments fraud?
Relatively few so far. The 2026 AFP Payments Fraud and Control Survey found only 17% of organizations currently use AI to combat payments fraud, even though 76% experienced attempted or actual fraud in 2025. That gap is part of why AI-supported detection offers an advantage today: most potential targets aren’t yet using it.
What is the most common AP fraud type AI helps prevent?
Business email compromise, where an attacker impersonates a supplier or executive to redirect a legitimate payment, was reported by 74% of organizations in the AFP survey. AI helps by flagging the resulting payment instruction as anomalous, for example a new bank account or an unusual amount, even when the email that triggered it looked convincing.
