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.

Read the full report: Merchant Underwriting in an Age of Bad Agents now.

Agentic payments are the hot topic today, but it’s agentic fraud that should be at the front of everyone’s mind. It represents a real and asymmetric threat that will have major impacts on the payments industry far before the earliest adopters are using agents to regularly make purchases. Failure to recognize and deal with the immediate problem of agentic fraud could not only help derail the success of the agentic payments everyone is distracted by, but it has the potential to usher in a golden era for payments fraudsters that could make ecommerce itself far less attractive to merchants and consumers.

In this article, I’ll examine the four key asymmetries that make agentic fraud so consequential for payments:

• Reliability asymmetry

• Cost asymmetry

• Information asymmetry

• Persistence asymmetry

I’ll also examine what an impending wave of complex, high-volume agentic attacks means for the future of fraud prevention in the industry.

In a game of ever-bigger walls vs ever-bigger ladders, every single aspect of agentic artificial intelligence currently favors the ladder builders. That’s going to have serious implications for the payments industry, because the threat and costs of agentic AI are immediate, whereas the potential payoff is many years down the road. At a minimum, the industry needs to move fast to make today’s best premium fraud prevention tools the baseline norm. At worst, a major victory for the bad guys may just be inevitable, even if temporarily.

Reliability Asymmetry: Bad Agents Can Fail Without Consequence, Good Ones Can’t

It’s hard to win when the playing field is tilted against you. The reality of security and fraud prevention is that bad actors don’t have to follow the same rules or meet the same standards that legitimate businesses do, and that gives them an advantage. Agentic AI takes that tilt in the playing field and pushes it to new extremes.

Today, the rollout of AI agents at companies is extremely limited. Recent research from Anthropic shows that the vast majority of agents on its public API only tackle low-risk, reversible tasks that fall well below the company’s own bar for “high autonomy.” Of all those agentic API calls, 73% still have a human in the loop and less than 1% involve an irreversible action.

The reason for this toe-dipping is simple: companies have regulatory, financial and reputational considerations that they must manage, and that creates limitations that they must adhere to. The agentic space is still the wild west, and agents, as they exist, represent serious risk. This is especially true in the payments space where questions of liability loom large. If an agent goes rogue or fails badly, it can have serious business consequences, so payments companies can’t just turn agents loose haphazardly and hope for the best.

Fraudsters face no such limitations.

While deployment of agents at scale represents a huge risk for companies, it involves effectively zero risk for fraudsters, who have no reputation to protect, no customers to keep happy, no compliance requirements and no laws to follow.

Operationally, agent failures are no concern for bad actors, because failure is the expectation anyway. Fraud is a numbers game in which perpetrators know that the vast majority of their financial gains will come from a very small sample of successful attacks. Agents represent a way for them to scale those attacks cheaply and easily, and even high rates of task failure or misalignment don’t really matter because attacks can just be run again, over and over. The potential ability of future agents to self-refine makes this an even bigger asymmetrical threat in favor of the bad guys.

Cost Asymmetry: Bad Agents Can Be Spun Up Far Cheaper Than AI-Powered Defenses

The cost of AI is currently a hot topic. GitHub Co-Pilot and Claude Code both recently moved to token-based billing that saw users’ costs increase anywhere from 20% to over 20x. We are currently in the phase of AI rollout where companies like OpenAI and Anthropic are getting ready to IPO and it’s time to face the music that, until now, everything we’ve done with AI has been heavily subsidized by VC money, and prices for access to top models are going to go up.

So, with subsidies running dry and things like token-based billing rolling out, how will bad actors be able to employ agents at scale without their costs ballooning out of control?

The answer lies in free, open source models. As previously explained, fraud and cybercrime are numbers games where all but a small percentage of attacks are expected to fail. That means bad actors don’t need cutting edge models to run persistent, agentic attacks – at least not for simpler schemes. Free frontier-class models like DeepSeek are more than powerful enough to drive agentic fraud. And, while proprietary frontier models like ChatGPT, Claude and DeepSeek have started to pull away again, open-source models that can be run locally are less than a year behind in capability – no issue when automated volume is the primary goal.

In contrast, there is huge pressure on companies to use frontier models. That requires absorbing the cost increases coming down the pipe as labs like OpenAI and Anthropic go public and face pressure to stop losing billions of dollars every year.

(Note: the recent SpaceX IPO prospectus laid these losses bare, showing the xAI division took $2.47 billion in losses on $818 million in revenue in Q1 2026 alone.)

For the payments industry, this represents a second huge asymmetrical disadvantage. Stakeholders will expect anti-fraud tools, and AI tools in general, to be running on cutting-edge AI, which will come at increasingly steep costs. At the same time, the bad guys will be more than happy to get by spooling up armies of agents using tools like OpenClaw and free models like DeepSeek or Qwen3.

Information Asymmetry: In the War Against Payments Fraud, Fog Favors Bad Guys

The AI industry as a whole is still very much a black box. Upcoming IPOs of pure-play AI companies may help shed some light on where the technology actually stands. But, today, there are far more questions than answers.

Where are the lines between real future capabilities, hype, marketing and downright deception? Who is liable for damage resulting from actions taken by autonomous AI systems? When will lawmakers around the world begin to regulate AI in earnest? What is the appropriate balance of regulation that achieves societal good without kneecapping advancement? Are LLMs a dead end on the path to theoretical AGI and what does that mean for business outcomes? What does the term “agent” even mean?

Today, we have no answers to (or even general agreement on) any of these questions. That puts companies in the extremely difficult position of trying to invest in AI to avoid being left behind without having any real idea of what tomorrow is going to look like technologically or legally. This creates the third asymmetry that tilts the fraud battlefield in favor of bad actors: there is a fog of war that really only impacts one side.

Companies must make critical decisions about how and where to direct their AI investments in an extremely dynamic, low-information environment. The stakes of this are low when it comes to adopting LLMs for simple tasks like summarizing meeting notes or drafting emails. They go up when it comes to critical operational tasks like adopting copilots. But they go way up when it comes to building and employing autonomous agentic systems. At best, getting it wrong means falling behind, but at worst, heavy investment without clear direction could be financially catastrophic.

Fraudsters, on the other hand, have no need for better information or strategic caution because they don’t care where the technology or the regulatory landscape goes tomorrow. They can employ the tools available today with reckless abandon, without worrying about things like accruing technical debt or disruptions to their operations if the rules change. The fog is not a hindrance to them, it’s cover. That allows fraudsters to be first movers, barreling forward at speed while the organizations on defense are leaned in, squinting, just trying to see the landscape a few feet ahead.

Persistence Asymmetry: It’s Easy for Fraudsters to be Persistent and Hard for Defenders

Advanced persistent threats (APTs) are highly sophisticated cyberattacks that are designed to constantly probe and chip away at security over a long period. They use techniques like polymorphism to re-disguise themselves continuously and adapt tactics as they build a picture of defense systems. If a DDOS attack is shock and awe, APTs are a siege.

Until recently, running APTs was so complex and costly that it was mostly a tool employed by state intelligence services. Anthropic reports that these actors have already been documented using Claude Code to streamline the process. But widespread access to cheap agents is going to change the barriers to entry entirely.

Agents are tools designed specifically for low-cost, repetitive action at speeds and scales humans can’t match, and that makes them ideal tools for APTs. But with open source models and agent management systems, anyone with the right technical knowledge can theoretically spool up home-brew attacks that closely resemble APTs.

Even without technical knowledge, Fraud-as-a-Service (FaaS) is already exploding. For example, in 2025, data from Recorded Future showed a 4x increase in scam merchant accounts over the prior year. This is directly attributable to FaaS and AI-powered boosts in everything from phishing to synthetic identity creation and beyond. Agentic FaaS is going to democratize APTs and make it possible for anyone with the desire to run complex, long-lasting, recursively improving attacks.

On the opposite side, AI-powered anti-fraud measures are fairly well equipped to handle the siege, but it’s still going to result in tighter screening, and a higher rate of “customer insult” – when false positives incorrectly deny or quarantine a legitimate customer’s payment. But processes like merchant underwriting are much less prepared for siege-style fraud, and this is where the persistence asymmetry really comes into play.

As Recorded Future’s data shows, more fraudulent accounts will slip through the cracks, which means re-underwriting will have to happen far more frequently, if not constantly, in order to catch bad sellers and long-term fraud like bust-out schemes. Automated underwriting systems can help with that, but it still costs money to run scans, especially if databases are being queried. That makes it much harder and more expensive for payments companies to run persistent defense than it is for bad guys to play persistent offense.

What Does Agentic Asymmetry Mean for the Future of Payments Fraud Prevention?

1. Fraud Defense Must Become Universally Persistent and Highly Adaptable

Agentic fraud will be both persistent and recursively adaptable, and in this case, the only way to fight fire is with fire. AI-powered anti-fraud tools, which today are still considered a premium value-added service (VAS), not only need to get better, but they must also become the baseline norm in all aspects of payments.

Every merchant will need to be on the most advanced real-time risk scoring and decisioning tools or they will see both successful fraud and false positives spike. Every merchant will need the most advanced dispute management systems or they risk getting buried in chargebacks. Payment providers themselves will need highly automated, persistent underwriting systems to stop the tidal wave of merchant account fraud that is already building. These tools will no longer be a question of efficiency or optimization; they will need to be the baseline layer if the industry has any hope of keeping up with the unparalleled volume and sophistication of agent-driven fraud that is years away at most.

2. Investment in Fraud Prevention Needs to Go Way Up at a Time When Merchants are Starting to Tighten Their Belts

In its 2026 Global eCommerce Payments and Fraud Report, the Merchant Risk Council found that merchants are starting to prioritize cost-minimization in their fraud management over improvements in outcome, including reducing attacks and improving the customer experience. The MRC data shows that, while over half of merchants still intend to spend more on anti-fraud tools over the next two years, the number is down seven percentage points over the prior year, and planned spending on staff/talent is way down.

Faced with rises in the cost of everything, merchants are starting to pull back from the fight against fraud at the exact time they need to be upgrading their defenses. This signals a wider cost problem and it means that providers looking to get better tools into the hands of merchants need to do one of two things: invest in making AI/ML-powered fraud prevention more affordable, or accept thinner or zero margins on advanced anti-fraud VAS.

3. There May be a Brief, But Unavoidable Golden Age for Fraudsters

There is a real possibility that the proliferation of asymmetric agentic fraud will, at least for a time, simply overwhelm the ability of merchants and payment providers to fight back. That could result in either a golden age or dark age of payments fraud, depending on which side of the line you’re on, with big impacts on the behavior of both consumers and merchants.

We already know that younger consumers are drifting back to brick-and-mortar, preferring the experiential aspects of in-store shopping. If the ecommerce fraud arena gets hostile enough, things like elevated risk of data breaches and transaction decline rates could result in a big migration away from ecommerce toward in-store environments perceived as less risky. On the merchant side, if the costs of fraud losses and the battle against them gets too high, it’s not difficult to imagine omnichannel sellers starting to deprioritize or even disincentivize web sales.

4. Companies Should Carefully Consider the Role of Consumer and Merchant Trust in Future Tech

Proliferation of agent-powered fraud will push both merchants and consumers towards payment methods they perceive as high-trust and high-safety. That could create an additional barrier to the adoption of agentic commerce. In addition to the issue of asymmetric reliability mentioned above, agentic commerce is currently in a low-to-no trust zone, with the complete absence of any guidance or regulation on questions like how consumers will be protected and who is liable for glitches and misaligned or rogue transactions.

Payment companies, up to and including the big card networks like Visa and Mastercard, understandably don’t want to get left behind if agentic commerce changes the way we shop to the same degree that ecommerce did. But, in a world where agents are going to benefit fraud and cybercrime far faster and far more extensively than they will consumers, trust could be at an even bigger premium and agents themselves may turn out to have a branding problem. Companies should consider this when forecasting how near agentic commerce adoption really is, because big investments that are too early can have the same consequences as big investments that are wrong.