Mark Beare, Head of Consumer, Malwarebytes.
In May, global payments platform Adyen, which processes transactions for companies such as Microsoft, Uber and Spotify, published its 2026 fraud report. Drawing on one of the largest datasets available, representing some $1.6 trillion in processed payments volume, the study revealed a paradox sitting at the heart of the digital economy: Fears about fraud may be costing companies more than fraud itself.
Most businesses are set up to measure the cost of fraud, not the cost of customers’ fear of it. They track fraud losses, chargebacks and security incidents. However, they’re much less equipped to measure the customers and revenue they lose because people are afraid of being defrauded or because the systems designed to protect them become obstacles themselves.
In a world increasingly shaped by AI and online fraud, this is a missed opportunity. To adapt, companies can and should start measuring fear and trust with the same rigor as revenue and begin treating them as quantifiable business variables rather than soft concerns.
In addition to asking how much fraud they’re preventing or what their chargeback rate is, executives might also ask how much legitimate revenue their security decisions are costing them and how many customers abandon them due to a lack of trust. Perhaps most importantly, they should ask themselves whether customers actually experience them as trustworthy.
Consider false declines. Roughly 50% of the merchants that Adyen surveyed reported an increase in the number of false declines—legitimate orders that overzealous fraud filters reject. That figure rises to 60% for companies operating in the Asia Pacific region.
This is the economics of fear in action, and it’s incredibly expensive. A 2025 merchant analysis by the fraud protection firm Riskified found that false declines cost businesses roughly three times as much as chargeback losses, which are the fees merchants absorb when banks reverse charges on customers’ behalf. Datos Insights, a financial services research firm, predicted in May 2024 that false declines would cost e-commerce brands $231 billion globally in 2026 and would rise to nearly $265 billion in 2027.
“The question is no longer how much fraud a business is willing to tolerate,” the Adyen study concluded. “The question is how much legitimate revenue it’s willing to lose trying to stop it.”
Unsurprisingly, customers who have been scammed become much more cautious, not just with their information but also with their behavior. According to LSEG Risk Intelligence’s March 2026 global survey of more than 21,000 adults, 97% of responding fraud victims said they changed their behavior after being scammed. They became more cautious about payments, more reluctant to share personal information and more broadly suspicious of digital communications.
The damage doesn’t stop there, though. To borrow a term from the military, fear is a force multiplier, and its influence extends far beyond people who have actually been scammed. Checkout research from the Baymard Institute, an independent e-commerce research organization, found that 19% of U.S. online shoppers who abandoned a purchase for reasons other than simply browsing said they didn’t trust the site with their credit card information. These customers were afraid of being burned and then acted on those fears.
Concerns about AI are adding another layer to this trust problem. Malwarebytes’ own June 2026 research found that AI-generated product photos or reviews had misled 22% of surveyed adults in the previous 12 months. According to a 2026 Usercentrics study of 11,000 customers in seven markets, close to half had taken at least one action with a direct financial consequence for a brand in the previous six months because of concerns about how AI was using their data.
This lost revenue is hidden within millions of abandoned transactions. There may be no chargebacks, fraud alerts or incident tickets. Unlike fraud losses, this cost rarely appears neatly on a dashboard, making it harder to measure and therefore more difficult to address.
However, trust can be built, and there’s financial value in doing so. The same Usercentrics study found that 52% of consumers would pay more for brands that are transparent about how they use AI with their data, with an average price premium of 7%. Among 18-to-29-year-olds, the share willing to pay more rises to 67%.
Businesses can start by understanding where trust is being lost and then take steps to rebuild it:
• Track false declines alongside fraud losses. Measure how many legitimate transactions your fraud controls reject, not just how much fraud they stop.
• Ask customers why they leave. Exit surveys can help identify when security, privacy or trust concerns are costing you a sale.
• Measure trust alongside conversion and retention. Ask customers how comfortable they feel sharing payment and personal information, then look at how those responses relate to purchasing and churn.
• Test the customer impact of security decisions. When you introduce new verification steps or fraud controls, measure what happens to legitimate conversion as well as fraud.
• Push your payment provider to give more details on decline reason. Sometimes, the data here is very vague, and you can get more if you ask. Also, most platforms are very fraud-sensitive, so have aggressive fraud settings enabled by default.
• Explain how you communicate. Be transparent with your customers about how you’ll communicate with them; which email addresses, websites and apps you use; and where they can go to verify a communication or get help.
The criminals are running a fear campaign, and its cost extends far beyond the money they steal. The executives who recognize that first will build stronger security programs, but they’ll also build organizations that measure trust as carefully as they measure revenue.
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