By Manish Vrishaketu, Chief Customer and Operating Officer, Tipalti
Teaser: The same AI tools making payment scams more convincing are now being used to stop them. Understanding both sides helps spot the warning signs.
Payment fraud isn’t new, but the tactics behind it have changed fast. In 2023, 80% of organizations experienced a payment fraud attack or attempt, according to the AFP’s Payments Fraud and Control Survey. Additionally, generative AI could push US fraud losses toward $40 billion by 2027up from $12.3 billion in 2023. It’s clear that AI is making payment fraud harder to spot, but it’s also one of the best tools for catching it. Fraud prevention can’t be a one-time check anymore; it has to be as adaptive as the technology it uses.
How Scammers Are Weaponizing AI
Most people were taught to spot fraud by the obvious mistakes, such as a poorly written email, a suspicious link, or an obviously fake voice on the phone. AI has eliminated a lot of those easy tells.
Voice cloning and video deepfakes can now recreate what a real person sounds and looks like closely enough to fool someone in a live conversation. In a business setting, that might mean an employee receiving what appears to be a video call from a company executive requesting an urgent payment to be sent immediately. The voice, the face, and the urgency all seem real because that’s exactly what the AI was designed to produce.
AI is also being used to build entirely fake identities. Scammers can generate convincing tax documents, bank records, and even functioning websites for a vendor or business that doesn’t actually exist. That’s often good enough to pass a basic background check, one that only looks at someone once rather than watching for problems over time.
Additionally, fraud that once required a large team of scammers can now be carried out by a single person with the right AI tools. One person can send out thousands of personalized phishing messages or fake invoices at once, each tailored using information scraped from social media or company websites. In some cases, scammers can even embed themselves inside a real, ongoing email conversation between a business and one of its actual vendors. They’ll watch for weeks before redirecting a legitimate invoice payment to their own account.
Growing Businesses Face the Biggest Risk
Growth makes this problem worse. The more a business expands, especially across borders, the more payments it sends, and the harder it becomes to catch fraud manually. Tipalti’s global payments research found that 87% of companies have already hit a point where their payment processes couldn’t keep up with how much they’d grown, and roughly a fifth of monthly global payments still require someone to manually step in and fix something.
Every one of those manual touchpoints is an opening for a scammer. A well-disguised fake invoice or vendor doesn’t need to fool a system; it just needs to slip past an employee handling too much volume to catch everything by hand. At that point, a fake vendor with convincing tax documents and bank records isn’t competing against a careful review; it’s competing against someone racing to keep payments moving manually. That’s exactly the weakness AI-generated fraud is built to exploit.
How AI Is Being Used to Fight Back
The same technology making fraud more convincing is also proving to be one of the best tools for stopping it, and in some ways, it may have the advantage. Fraud detection benefits from constantly learning and improving over time in a way that a single scam attempt doesn’t. A few examples of how this shows up in practice:
- Vendor monitoring doesn’t stop at onboarding anymore. AI keeps watching for red flags for as long as the relationship lasts.
- AI compares a new vendor against thousands of others at once, flagging cases where unrelated businesses share the same bank account or tax ID, a common sign of one person running multiple fake identities.
- A payment request from an unfamiliar device, location, or time of day gets flagged automatically, instead of relying on someone to notice by chance.
- Before a payment goes out, AI checks that the invoice and purchase order match, closing a hole that fraud has long exploited.
- Every review and approval gets timestamped, creating a trail that makes it much harder for a single compromised approval to slip through unnoticed.
None of this replaces good habits on the ground. A few steps make the biggest difference:
- Eliminate manual processes: Start with the highest-risk workflows, replacing spreadsheets and email-driven approvals with AI-powered automation.
- Choose secure platforms: Work only with providers that build in fraud detection, vendor verification, and compliance controls.
- Train employees regularly: Ongoing training on phishing, invoice fraud, and deepfake scams matters more than a single onboarding session.
- Require dual approval for high-risk changes: A single person shouldn’t be able to update a vendor’s payment credentials; changes like that need a second set of eyes.
- Validate vendors continuously: Ongoing monitoring, not one-time onboarding checks, is what actually catches fraud.
- Build incident-response readiness: Have a playbook in place for fraud incidents (including recovery and communication plans) before you need one.
Fraud Prevention Has to Keep Pace
AI didn’t invent payment fraud, but it made it faster, more convincing, and easier to scale. It’s also made fraud detection just as fast and just as capable, catching what old warning signs would have missed. Staying ahead of this isn’t about waiting for scams to slow down, because they won’t. It’s about building fraud prevention that adapts as quickly as the fraud does. That’s exactly what companies like Tipalti have been building toward as global payment volumes, and the fraud attempts riding along with them, keep climbing.
Manish Vrishaketu is Chief Customer and Operating Officer at Tipaltiwhere he has helped customers transform their finance operations for more than 10 years.