After years of quiet escalation, business leaders are finally beginning to grasp just how serious the threat of fraud has become. Today, almost half of all fraud attempts (41%) involve artificial intelligence. Nowhere is this more evident than in the payments industry. Fraudsters can use AI to generate convincing fake invoices, purchase orders, and payment instructions that mirror legitimate business documents. I’ve seen examples that are indistinguishable from the real thing, which is a tremendous concern for customers handling high transaction volumes or complex approval chains.[1]
Even more unsettling is the rise of deepfakes. Scammers can now produce realistic voice and video impersonations of CEOs and senior executives, tricking employees into transferring vast sums of money. In the US alone, there were over 105,000 deepfake-related attacks last year, amounting to one every 5 minutes and impacting major global brands, including Ferrari and WPP.
One case involved the UK engineering firm Arup, where an employee transferred $25 million after a video call featuring AI-generated replicas of their leadership team. It’s no wonder the US Treasury Department has raised urgent warnings about this rapidly evolving threat.
The digitalization of B2B transactions has unlocked transparency, speed, and convenience, but it’s also opened the gates to increasingly sophisticated cybercrime. The key challenge now is how to shut those gates again without stifling progress. Traditional ERP systems often lack the advanced fraud-detection capabilities needed to address these evolving threats. They were designed long before AI-engineered fraud emerged.
The speed of AI fraud means real-time monitoring and analysis are now essential components of effective fraud prevention strategies. But analyzing transaction data, identifying anomalies, and flagging suspicious activities for immediate review is proving too large a task for manual processes.
Another key challenge in combating AI-enabled fraud is maintaining operational efficiency. Paradoxically enough, being overly cautious, refraining from working with a new company out of suspicion rather than actual fraud, can be just as damaging as falling victim to fraud itself.
Smart technology can be a powerful tool and help minimize fraud losses. Businesses that experienced fraud reported losing an average of 3.5% of annual sales, with small firms losing up to 5% Yet, organizations that invested in automated, proactive anti-fraud solutions saw those losses fall to just 2%
Companies relying on manual or reactive measures lost around 4.5% of revenue, often due to slow verification, human oversight, or inconsistent identity checks. The data clearly points to automation as a key differentiator: around half of organizations using digital identity verification tools report being very or extremely satisfied with their fraud prevention strategies, compared with just 17% of those using manual systems.
While AI has supercharged the fraud problem, it also provides the most promising path forward. The solution lies in leveraging the same technology for defense and using machine learning to spot anomalies, verify authenticity, and flag risk in real time. Many payment leaders are already employing AI to validate invoices, purchase orders, and payment instructions before they’re processed. At TreviPay, we actively use AI to detect suspicious activity early across B2B payment networks. AI may contribute to fraud, but the same technology that enables deception can also enable protection. With the right tools, partners, and vigilance, we can stop this threat.
This article is shared at no charge for educational and informational purposes only.
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[1] https://www.cybersecurityintelligence.com/blog/ai-is-fueling-payment-fraud--8899.html
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