“Customer insult” is on the rise, and it has big costs.
How can merchants catch more fraud while stemming the growth in false positives?
What’s worse, letting a fraudulent transaction slip through the cracks, or blocking a good transaction in the name of caution? This is an important question because false positive declines are on the rise in ecommerce.
A false positive is a legitimate transaction incorrectly flagged as fraud. It’s also called “customer insult,” and with good reason – research shows that almost half of customers who experience an incorrectly flagged transaction will never return to that merchant. And with many ecommerce merchants experiencing false positive rates of 5% or greater, and some even exceeding 10%, there is a real risk that clamping down on fraud could actually be costing merchants more than the fraud itself.
This article takes a deep dive into false positives, including why they’re such a big problem for ecommerce merchants, why they’re on the rise, and what tools merchants can adopt to reduce them while continuing to fight back against payments fraud.
Customer insult is a serious problem for ecommerce merchants.
The 2026 Global eCommerce Payments and Fraud Report by the Merchant Risk Council (MRC) and Visa offers some key insights into how false positives are impacting merchants.
In 2026, payment success rate was the number one metric ecommerce merchants rated as extremely important, leapfrogging revenue for the top spot. Simply put, merchants are increasingly worried about how often payments fail, and false positive declines are a huge part of that problem.
Overall, 78% of ecommerce merchants told Visa/MRC they had customer insult rates of 2% or more. 27% of merchants reported rates between 5% and 10%, and 13% of merchants reported staggering rates of 10% or greater.
The obvious first-order consequence of declining anywhere from two to over ten percent of transactions is the immediate revenue loss. But research shows that roughly 40% of shoppers who are hit with a false positive decline not only abandon that sale, but also don’t return to shop with that merchant in the future. That means almost half of false positives are resulting in loss of the complete future lifetime value of the customer.
The average customer lifetime value (CLV) for an ecommerce brand is often cited in the $100–$300 range, but there is rarely any reliable data offered to back up those numbers. In reality, CLV is highly unique to each merchant. But it isn’t hard to imagine that for luxury brands or merchants with high repeat purchase rates, CLV could easily be in the thousands of dollars. The MRC data means unnecessary losses of that scale could be happening on anywhere from ~0.8% to over 4%+ of all transactions. That’s a huge leak in the overall return on merchants’ investments in fraud management.
False positives are on the rise because the evolution and costs of fraud are outpacing merchants’ ability to keep up.
The number of merchants experiencing false positive rates of 2%-10% is up almost 23% from Visa’s 2024 Global Payments and Fraud Report. There are a number of reasons for that increase, including more sophisticated fraud, tighter and overly static fraud screening rules, and underoptimized use of tools that could help solve the problem.
Fraud management is failing to keep up with the evolving sophistication of fraud.
Fraud itself is changing, and that’s putting strain on all but the most advanced screening systems. For example, the emergence of Fraud-as-a-Service and widespread use of shared infrastructure can cause outdated fraud defense stacks to incorrectly and automatically treat huge volumes of traffic as fraud. VPNs are another common problem. Fraudsters now commonly abuse consumer VPNs and, as a result, many anti-fraud tools will automatically flag a transaction as soon as they detect a VPN. But about a third of all adults in the U.S. use one, including 40% of Gen Z.
As AI tools help fraudsters become even more sophisticated, these kinds of gaps where systems lag evolving tactics will continue to have a huge impact on false positive rates.
Merchants are going too far with rigid, rules-based fraud screening.
The struggle to keep up with evolving fraud is real and, in MRC/Visa’s 2026 report, adapting and staying up to date was the most cited challenge category among ecommerce merchants. As merchants and fraud management teams fall behind the threat, one natural response is to simply clamp down on the rules triggering the basic filters they already use
The instinct to turn to no-tolerance policies is understandable as the costs to remediate fraud and disputes rise. According to LexisNexis, in 2026, fraud now costs an average of $5 in total for every $1 of direct loss. Fighting first-party chargeback fraud is also getting more expensive, exceeding $80 per dispute for the first time ever. But, like a finer net that catches more fish but also catches more of everything, clamping down on static screening rules is a guaranteed way to force false positive rates up, and with today’s technology, it’s an archaic tactic.
Far too many merchants aren’t using the best tools, likely due to cost or complexity.
MRC/Visa’s 2026 data shows an alarming trend in fraud monitoring and tool use. The number of merchants monitoring for fraud at key points in the customer journey is falling. Only 51% said they actively monitor during checkout and less than half monitor during disputes. Even monitoring during payment is down to 57%. What this means is that it’s becoming a challenge to get merchants to monitor at all, let alone to adopt the most advanced artificial intelligence and machine learning (ML) enabled systems. As a result, most merchants who are actively screening are using outdated tools.
The reality is that many merchants see modern fraud management as expensive and overly difficult. MRC/Visa found that, for the first time ever, almost a third of all merchants now cite minimizing fraud-related operational costs as more important than reducing fraud or improving customer experiences. That’s 3x higher than 2024 and a clear signal that the cost of fraud management is an issue. The complexity of systems is another issue, and fraud orchestration was cited as a key area that needs improvement by almost half of merchants.
Unless it gets easier and cheaper for merchants to adopt advanced tools, they’ll continue to rely on simple rules-based screening, and false positive rates will continue to rise.
What merchants can do to reduce their customer insult rate while still fighting back against next-gen payments fraud.
Action Step: Adopt the latest AI/ML-powered fraud screening tools.
The most effective way merchants can reduce false positives is to ensure they’re using the most up-to-date AI/ML-powered tools and capturing more data points to use in decisioning. Just some of the tools merchants should be using include:
Pre-authorization AI/ML fraud detection: ML significantly improves the quality of scoring that can be done before a transaction is sent for authorization so it can be challenged further or declined before incurring fees.
Impact: Reduces false positives by analyzing transactions more deeply and dynamically than simpler rules-based screening.Positive behavior models: ML systems model what good customer behavior looks like and then positively weights orders closely matching those behavior patterns. The higher the positive behavior score, the stronger the signal to send the transaction for authorization.
Impact: Reduces false positives by balancing verifiably positive behaviors against other potential flags.Device fingerprinting: Device fingerprinting builds a profile of a customer’s device that can be tracked across multiple sessions. That helps ensure a customer is who they claim to be. ML-powered fingerprinting can catch advanced tricks like device spoofing and keep up with fraudsters across multiple accounts, even if they’ve cleared their cookies.
Impact: Reduces false positives by whitelisting friendly device fingerprints while blocking flagged ones.Identity intelligence: Identity intelligence networks build profiles of the user identities behind transactions and use the enormous amount of data captured across the network to score transactions. For example, Kount uses Equifax’s Digital Identity Global Network, which uses data from 60 billion consumer interactions to identify good and bad users.
Impact: Reduces false positives by whitelisting identities that have a clean record across the network.Behavioral biometrics: ML passively monitors how a user interacts with their devices. It analyzes things like typing speed or touchscreen and gesture patterns and builds a profile unique to the individual from thousands of data points.
Impact: Reduces false positives by identifying signals that can override low or medium-risk fraud flags if the transaction closely matches the user’s known behavioral fingerprint.Negative behavior scores: The flipside of positive behavior models, where ML maps on-site customer behavior against known use patterns of fraudsters and automated attacks.
Impact: Enables screening rules to be loosened while still catching fraud based on high-risk or botlike behaviors.
Action Step: Use 3DS as a step-up challenge instead of automatic declines.
Rather than automatically declining or quarantining transactions that flag as potential fraud, merchants should use a multi-layered step-up verification using 3D Secure. 3DS services, like Verified by Visa and Mastercard Identity Check, verify a customer’s identity using an additional authentication step like a one-time code, biometric scan, or an authorization request pushed to the customer’s banking app. This adds some additional friction to a transaction, but it’s a minor annoyance compared with the potentially relationship-ending frustration of customer insult.
3DS has seen enormous success in the European Union and United Kingdom, where it’s a mandatory security feature on most card-not-present transactions. But merchants all around the world can activate it through their payment service providers (PSPs) and modern fraud screening tools make it easy to set 3DS up to ask for verification only when certain flags or thresholds are triggered. That step-up challenge represents a great way to turn false positives into successful authorizations with minimal impact on the customer experience. Better still, a positive authentication through 3DS can be logged as part of a customer’s digital fingerprint, allowing future transactions to be whitelisted, as well.
Action Step: Look for one-stop, out-of-the-box orchestration.
Fraud management is no longer simple, but orchestration doesn’t have to be difficult. Many providers now offer highly orchestrated suites of anti-fraud tools that merchants can easily integrate into their payments, either as simple third-party API connections or directly through their PSPs. These platforms not only improve fraud outcomes through extensive network effects and multi-tool integrations, they also reduce the complexity and cost of managing fraud, especially for smaller merchants that can’t afford dedicated in-house teams. That solves a major pain point and helps ensure merchants aren’t just turning away from fraud monitoring altogether to save time, money or headaches.
Platforms like Kount, Signifyd, Riskified, and Forter all offer highly capable suites of tools that go beyond simple risk scoring and incorporate multiple AI/ML tools that cover the full lifecycle from checkout to payment to disputes. Merchants still trying to piece together their own layered fraud defense stacks from disparate tools are taking on work that doesn’t really need to be done, and moving to a natively-integrated platform can significantly reduce false positives.
For PSPs, failing to offer frictionless connection to one or more of the platforms mentioned above is failing to address a top merchant pain point, and a major competitive disadvantage. Adding one-stop integration with an extensive fraud defense platform should be a top priority.
Conclusion
The entire fraud space is evolving rapidly, and merchants are struggling to keep up. As fraudsters and their attacks get more sophisticated, defending gets more complex and, in many cases, more costly. That’s creating an environment where merchants paralyzed by options and orchestration challenges are trying to catch fraud with the simplest, least discriminating tools. That, in turn, causes major problems with false positives.
Without a solution, merchants are faced with the choice between losing out on important customer relationships through false declines or dialing back on their fraud prevention and letting bad actors slip through the cracks. That’s a poor set of options. It’s also a false dichotomy, because there are plenty of tools today that can both choke out payments fraud and reduce false positives. Payments, as an industry, just needs to find better ways to get those tools into the hands of more merchants. That’s going to mean finding ways to make it faster, simpler, and cheaper for merchants to adopt extensive and pre-orchestrated fraud platforms.
Because only when staying at the cutting edge of anti-fraud technology is cheap and easy will the rate of adoption near the 100% level it really should be at, and only then will false positive rates start to shrink significantly.
Ryan Healy is a fractional senior content writer who works with companies in payments and fintech. He primarily ghostwrites long-form reports and executive thought leadership for his partners. His edge is his ability to synthesize complex information into content that delivers value and human beings actually want to read. He's fueled entirely by the 23 glorious flavors of Dr Pepper. Visit DPIntel.com for more of his writing.