New PYMNTS Intelligence research in the April 2026 Payments Innovation Tracker^® Series, a collaboration with Paymentology, finds 48% of consumers are at least somewhat interested in artificial intelligence agents doing their grocery shopping or planning their meals for them, while the same share would let an autonomous assistant manage their subscriptions, and 44% are somewhat interested in using the tech for buying gifts.

But as agentic AI systems evolve from passive advisors into active economic participants, a fundamental shift is revealing itself. Machines are beginning to transact on behalf of humans. And when artificial intelligence gets a wallet, the center of gravity in commerce moves decisively away from the checkout page toward an experience not shaped by better recommendations or more personalized ads, but by delegation.

The new battleground is not the moment of purchase. It is the infrastructure that governs how AI spends money. In such a world, the traditional markers of commerce such as browsing, comparing, checking out, and choosing shipping may quickly fade into the periphery. What remains instead could be a network of agentic AI systems that can coordinate economic activity on behalf of individuals.

Why the Payment Layer Is Becoming Agentic Commerce’s New Frontline

The transition from recommendation to execution may appear incremental, but it represents a structural break. Traditional eCommerce models rely on influencing human intent: surfacing the right product, at the right time, at the right price. AI-powered recommendation engines refined this process by compressing discovery into algorithmic prediction.

In this model, the human user becomes less of a decision-maker at the point of sale and more of a policy-setter upstream. Preferences are codified, spending limits are defined, and trust is extended to the system. The act of shopping becomes asynchronous, continuous and largely invisible.

The report’s findings reveal that, as AI agents take control of purchasing decisions, the payments layer emerges as the critical control point. This is where authorization happens, where rules are enforced, and where trust is negotiated between human, machine and merchant.

Card networks, issuing banks and FinTech platforms are rapidly repositioning themselves to capture this moment. Their goal is not merely to process transactions, but to define the frameworks through which artificial intelligence can transact safely and effectively.

Read the report: The Intelligent Spend Shift: How Card Platforms Can Prepare for Agentic Commerce 

This involves more than enabling API-driven payments. It requires building systems that can interpret and enforce user intent at scale. For example, an AI agent might be authorized to reorder household essentials automatically, but not to make discretionary purchases above a certain threshold. It might prioritize vendors with specific sustainability credentials or avoid subscriptions that do not meet predefined criteria.

Embedding these rules into the payments infrastructure transforms it into a governance layer. The payment credential becomes a programmable instrument, capable of encoding policy, context and constraints.

This does not mean checkout disappears entirely. But for routine transactions, checkout may ultimately, in an agentic landscape, become a back-end process, invisible to the user and optimized for machine interaction.

This shift challenges longstanding assumptions about conversion optimization. Metrics like cart abandonment rates lose meaning when carts are no longer assembled by humans. Instead, the focus may move to machine-to-machine efficiency such as benchmarks around latency, reliability, compliance and interoperability.

The competition is not just about processing volume. It is about owning the interface between human intent and machine execution. After all, when AI gets a wallet, commerce does not simply become faster or more convenient. It becomes fundamentally restructured. The question is no longer how to win at checkout. It is how to become the system that checks out for everyone else.

A new report from PYMNTS Intelligence and FIS finds issuer processing is evolving from what was traditionally a back-end utility into a “decisioning layer” that determines how payments are approved, optimized and secured in real time. Commerce itself is becoming more automated, personalized and AI-driven, especially as agentic commerce models emerge.

The report’s central finding is that not all data are equal. Historical transaction records alone are no longer sufficient for modern payments environments where AI systems increasingly initiate or influence transactions.

Issuers and processors are being pushed to aggregate behavioral, contextual and real-time operational data that can improve authorization decisions, reduce fraud and minimize checkout friction.

Traditional issuer processing systems were built primarily for execution reliability and speed, according to the report. But those rules-based systems struggle in environments where transactions are increasingly shaped by contextual signals, consumer behavior and AI-enabled commerce flows. The report estimates that issuer false declines contribute to roughly $30 billion in annual lost sales globally.

From Processing Transactions to Orchestrating Decisions

That friction becomes even more problematic in agentic commerce environments, where artificial intelligence agents may eventually initiate purchases on behalf of consumers without the traditional cues associated with card-present transactions. The report notes that payments are no longer simply triggered by users but increasingly by systems acting on their behalf, elevating the importance of infrastructure capable of evaluating and authorizing those transactions in real time.

The data requirements for that future are extensive. Machine learning systems rely on transaction histories, risk indicators, spending patterns, behavioral insights and contextual data to make effective decisions. The report found that 47% of organizations still struggle with poor-quality data that limits AI effectiveness in decision-making.

For agentic commerce specifically, the report points to several critical data types that will become increasingly valuable.

Behavioral data is one of the most important because artificial intelligence systems need to distinguish legitimate autonomous activity from fraud or anomalous behavior. Spending histories and transaction patterns help create models capable of recognizing when an AI agent is operating within expected consumer preferences.

Credential and authentication data also become essential. In agentic commerce, AI systems may need to validate payment credentials, apply spending controls and execute transactions without direct user intervention. That raises the stakes for identity verification and real-time authorization frameworks.

Operational and contextual data are equally important. The report notes that more modern payment data is emerging as a real-time signal of customer behavior and operational performance. That includes device information, timing patterns, merchant characteristics and ecosystem-level connectivity data that help determine whether a transaction should proceed.

The report’s findings indicate that firms capable of aggregating and activating those data streams at scale will gain a significant competitive advantage. Rather than merely processing payments, issuer platforms are evolving into orchestration layers that connect data, processing and ecosystem participants in real time.

Agentic Commerce Raises the Stakes for Data Readiness

Still, the report makes clear that many organizations are not fully prepared for the transition to data-centric payments infrastructure.

One challenge is fragmentation. Legacy systems often isolate fraud tools, authorization systems and customer data into separate silos, limiting the ability to create unified, real-time intelligence layers. Another issue is activation. Simply collecting data is no longer enough. The report emphasizes that the differentiator increasingly lies in operationalizing data into actionable insights that can improve approvals, reduce false declines and adapt dynamically to changing conditions.

To prepare for agentic commerce, the FIS and PYMNTS Intelligence report outlines several areas where organizations need to focus. Those include improving how issuer data is captured and integrated, investing in platforms that support real-time decisioning, strengthening ecosystem connectivity and leveraging AI analytics to improve fraud management and authorization outcomes.

The broader implication is that payments infrastructure is shifting from systems of record to systems of action. In an AI-driven commerce environment, the winners may not necessarily be the organizations with the most data, but those most capable of transforming fragmented information into real-time intelligence that supports autonomous, secure and frictionless transactions.

When fraud attack rates fall, the instinct is to celebrate. One might think fewer attacks means better defenses and better outcomes. But a close look at recent fraud trend data tells a more complicated story, and fraud teams that read the headline number without reading the fine print may find themselves caught off guard.

Payment fraud attack rates declined 14% year over year, and manual review volume dropped 17%; yet over the same period, overall chargeback rates climbed 56%. The two trends are connected, and showcase that fewer attacks doesn’t mean fraud risk is declining overall.

Precision has replaced volume

For the last few years, merchants have faced a steady increase in fraud attempts volume as AI and automation made it easier for bad actors to attack at scale. As a result, companies fortified their defenses, and that calibration seems to be helping, since payment fraud and manual review rates have decreased.

However, what the data now shows is that fraudsters are deliberately targeting what they believe to be high-volume accounts with stored credentials and accumulated balances that offer higher payoff per attempt. 21% of consumers report experiencing account takeover in the past year, often alongside payment fraud.

The metrics that teams traditionally used to evaluate fraud program health were built to measure volume. When the threat changes shape but the measurement framework doesn’t, teams can end up optimizing for the wrong outcome.

The “good user gone bad” pattern illustrates this well. Attackers build account credibility over time, establishing normal behavior and generating positive transaction history before executing a high-value fraudulent action, such as adding stolen payment credentials to a trusted account, weeks or months later. A fraud model evaluated solely on immediate transaction outcomes will miss this entirely. The account looks fine until it doesn’t.

Efficiency gains aren’t the same as risk reduction

The 17% decline in manual review volume reflects real operational progress: stronger automation, more precise decision logic, less friction for legitimate users. But when review volume falls while chargebacks rise simultaneously, that combination can indicate decision thresholds are well-tuned for speed but not fully calibrated for more sophisticated attack patterns.

Reducing manual review should free analysts for complex, high-value investigation. When efficiency metrics and chargeback loss metrics simultaneously increase, merchants should audit where automation is working effectively and where human judgment can reduce some of these more complex fraud attempts.

Industry context changes everything

Fraud exposure doesn’t distribute evenly across business models, and comparing rates to a broad industry average can lead teams to the wrong conclusions. A 3.7% payment fraud attack rate in food and delivery reflects the speed-first, high-volume nature of that environment. A 0.4% attack rate in online gambling sounds low, until you account for the fact that high-value transactions concentrate dispute risk in ways that raw attack volume doesn’t capture.

Internet and software companies face elevated chargeback exposure for a specific reason: subscription models and stored credentials are convenient for fraudulent and legitimate users alike. The same features that reduce checkout friction also lower the barrier for account compromise. This is why peer benchmarking matters more than absolute numbers. A rate that looks alarming in isolation may be typical for a specific segment, while a rate that looks clean may signal under-detection.

The trust dimension

Fraud’s impact on consumer behavior rarely makes it into internal loss calculations, but it belongs there. When 52% of consumers say they would stop using a platform after experiencing fraud, and 73% say they’ve abandoned a purchase due to payment security concerns, fraud goes from a finance problem to a growth problem.

Losing a customer to fraud isn’t only a one-time transaction loss. It’s the cumulative value of everything that a customer would have spent, plus the reputational cost when trust erodes publicly. Customers rarely distinguish between attacker behavior and platform responsibility. If an account gets compromised, the brand absorbs the perception hit.

Measuring the right things

The practical reality is that a single metric, however directionally encouraging, doesn’t tell the full story. The more useful questions are:

• Is chargeback exposure tracking with attack rate trends?
• Are manual review resources concentrated where complexity warrants them?
• Is account-level risk being measured longitudinally rather than only at the transaction moment?

Fraud concentrates where incentives are highest and where defenses haven’t kept pace. The teams best positioned to stay ahead are the ones asking whether the numbers they’re watching are the right ones.

But at the far edge of the digital economy, Mother’s Day has quietly become a glorious, over-caffeinated payments event: part emotion, part logistics, part luxury checkout flow. The flowers and the card still matter, but so do the reservation link, the deposit request, the gift-card balance, the card-on-file confirmation and the little thrill of pretending a $61,500 trunk is really about “memory-making.”

The latest available numbers from the National Retail Federation (NRF) show why the holiday has become such a rich commerce story. In 2025, Mother’s Day spending was expected to reach $34.1 billion, with 84% of U.S. adults planning to celebrate. Online was the top shopping destination at 36%, while jewelry was the largest dollar category at $6.8 billion, special outings reached $6.3 billion and gift cards hit $3.5 billion. Nearly half of shoppers said they wanted something unique or different, and another big chunk said they were looking for gifts that create a special memory.

At the ultra-high end, the “things” side of the ledger is less about buying Mom one more nice object and more about buying her a better anecdote. A five-object fantasy board starts with Cartier’s Panthère de Cartier watch, because it is the rare gift that reads simultaneously as watch, jewelry and inheritance strategy; Cartier lists a small yellow-gold model at $27,000.

Then there’s the De Beers Talisman Locket, priced at $37,100 and notable because it is a limited series of 20 pieces with engraving potential. It’s less of a present, and more “future family lore.” For maximalist delight, Louis Vuitton’s LV x TM Music Trunk is the showstopper: a Murakami-splashed trunk with a compartment for a turntable and storage for vinyl, which is what happens when nostalgia gets a budget comparable to that of an Audi.

A smarter, slyer flex is Lalique’s Tourbillons vase, a $5,000 crystal classic first designed in 1926: especially notable because it turns the most traditional Mother’s Day gift, flowers, into a permanent upgrade. And for the mom whose taste runs more modernist than maison, Bang & Olufsen’s Beosound 2 starts at $4,000 and makes a persuasive case for premium audio as a sculptural home object.

The experience side is where Mother’s Day goes fully cinematic. Four Seasons’ World of Wellness 2026 private-jet itinerary is the obvious headline act: 20 days and eight destinations at a total sticker price of $188,000 per adult.

If Mom prefers sea air to airport lounges, Aman’s Amandira private yacht and its Komodo expeditions are almost comically well-appointed: a 52-meter yacht, five cabins, a crew of 14, private chefs, a dive master, a spa therapist and itineraries built around dragons, reefs and bragging rights.

Then there is Belmond’s Royal Scotsman Dior wellness journey, because once you accept the existence of a Dior spa carriage rolling through the Scottish Highlands, resistance feels provincial. Belmond says the spa carriage has two treatment rooms styled in Dior’s burgundy toile de Jouy, and the concept has already expanded into multi-day retreat programming.

Golden Door’s Golden Flight is another splendidly over-engineered choice: door-to-door transfers, private plane access, curated wellness kit and a seven-night all-inclusive stay that begins before takeoff.

Finally, The Ritz-Carlton Yacht Collection’s Mediterranean voyages are currently listing seven-night sailings such as Monte Carlo to Rome from $11,100, which is useful for anyone hoping to frame “ultra-luxe yacht trip” as a heartfelt family gesture.

And that, really, is the payments angle. Mother’s Day at the top end is orchestration in its finest form: eCommerce checkout, concierge confirmations, trip deposits, premium-card points, dining reservations and experience spending all braided into one sentimental shopping occasion.

The latest NRF data makes clear that consumers already want uniqueness and memory-making; luxury brands are simply monetizing that desire at a level normally reserved for real estate and small watercraft.

So yes: payments plays in Mother’s Day the same way bass plays in a song — often behind the melody, but doing more work than it gets credit for. The flowers aren’t gone, they’ve just been joined by yachts, wellness jets, designer spa cars and a turntable trunk that could anchor its own inheritance dispute.

Which feels, in its own extravagant way, exactly right for a holiday built around trying to repay someone for everything, with one very nice click.

When it comes to driving eCommerce revenue, many retailers overlook the fundamentals behind the customer experience. Before investing in new AI tools or expensive marketing campaigns, conduct an audit of your current customer journey.

The eCommerce customer journey includes five stages: Awareness, Consideration, Purchase, Retention and Advocacy. Each stage provides an opportunity for you to deliver value, earn trust and build loyalty.

Awareness

Customers find your website through channels like social media, word of mouth, paid ads or traditional media. Fine-tuning your marketing strategy and optimizing your site for discovery are most critical here.

• SEO: Focus on helpful content, fast page load speeds, quality images, descriptive title tags and mobile-responsive design.
• GEO (Generative Engine Optimization): Optimize content for AI-powered search engines like ChatGPT and Google AI Overviews by answering FAQs clearly, using header tags, bullet points and conversational language with long-tail keywords.
• Structured Data: Adding structured data enables rich search results that boost click-through rates and supports both SEO and GEO performance.

Consideration

During the consideration stage, potential customers research your brand and compare you against competitors. Key optimizations include:

• Site Architecture: Use category-level navigation and breadcrumbs to help customers find products and reach checkout faster.
• Site Accessibility: Maintain compliance with WCAG and ADA guidelines.
• Product Discovery: Enable category and search filters and invest in a robust on-site search with mobile-first design, autocomplete and alternatives to “no results” pages.
• Ratings and Reviews: Collect reviews proactively, allow photo/video uploads and engage with reviewers regularly. If you’re looking for a ratings and reviews platform to increase eCommerce revenue, book a demo of Revere.
• Question and Answer: Q&A features on product pages address common customer concerns and generate keywords that improve discoverability.

Purchase

The purchase stage is where a customer completes a transaction on your site, and streamlining checkout is critical.

Low-Friction Checkout

The average global eCommerce cart abandonment rate is around 70%. Below is a basic checklist of requirements that you should ensure you meet to reduce checkout friction:

• Optimize site speed and mobile checkout with one-tap payments
• Offer guest checkout
• Display return and shipping policies clearly
• Include SSL certificates and payment security badges
• Be transparent about taxes and fees

Personalization

Personalizing the eCommerce customer experience also drives conversions – use first-party data to power cross-sells and upsells, offer wish lists for shoppers and include urgency badges like “low stock” and “# others have this in their cart.”

Retention

The customer journey doesn’t end at purchase. During the retention stage, customers are deciding if they will become repeat shoppers with your brand.

• Gamification: Quizzes, points, challenges and progress tracking keep customers engaged and coming back.
• Loyalty Programs: 77% of US consumers are loyal to brands with rewards programs, and 81% of free loyalty program members buy more frequently.
• Customer Service: According to HubSpot, 93% of customers are more likely to make repeat purchases with companies who offer excellent customer service. Include customer service contact information in all post-purchase communications, utilize chatbots and re-share FAQs about your return policy and order tracking information.
• Returns: 92% of customers who have a hassle-free return experience will make another purchase from the same retailer. Offer options like box-free and label-free returns. And reduce returns by clearly displaying your return policy on your site and including features like fit guides and 360-degree product views on your product detail pages.
• Post-Purchase Outreach: Keep your brand top of mind for customers after they make a purchase. Send review requests, encourage customers to follow your brand on social media and remind them to sign up for your loyalty program.

Advocacy

In the advocacy stage, happy customers become brand evangelists. Reward their advocacy with exclusive rewards and deals, amplify UGC in your marketing and run branded hashtag campaigns to show appreciation.

Looking Forward: Next Steps for Revenue Growth

There is no one-size-fits-all approach to eCommerce. Your site should reflect your unique brand and work for your customers.

For more than 25 years, Virid has helped leading retail brands like Journeys, Tory Burch and Johnston & Murphy grow their eCommerce businesses with an average 51% increase in online revenue in the first year. Sign up for a free eCommerce site evaluation to find out what’s holding your growth back and how to fix it.

The report, “Orchestrating Trust: The Future of Fraud Prevention in Payments,” argues that fraud management is entering a new phase. Digital commerce has created a complex network of payment gateways, identity tools and security systems. When these tools operate independently, merchants risk higher false declines, added friction and lost sales. Fraud orchestration platforms aim to unify those signals into a single decision layer, helping organizations respond to threats faster while improving the customer experience.

The research finds that the next generation of fraud prevention focuses less on a single detection tool and more on coordination. Modern fraud defenses combine behavioral data, device intelligence, machine learning models and payment routing into an integrated workflow that can assess risk in milliseconds. The goal is to protect transactions while keeping legitimate purchases moving through checkout.

Key Findings From the Report:

• 85% of merchants say reducing friction for legitimate customers is their biggest fraud-prevention challenge. Companies increasingly see customer experience as part of the fraud equation rather than a separate concern.
• 53% of U.S. financial institutions already use fraud orchestration or expect to adopt it soon. The technology is moving from experimentation into mainstream deployment across banks and payment providers.
• 51% of global eCommerce merchants expect spending on fraud-management staff to remain flat or decline. At the same time, most plan to increase investment in automated fraud technologies that can scale more efficiently.

The third finding highlights a structural shift in how organizations approach fraud management. As digital payments grow, companies are under pressure to control operating costs while responding to more sophisticated attacks. Automation is filling that gap.

Fraud orchestration platforms help companies do more with fewer resources. These systems connect application programming interfaces, third-party risk signals and internal transaction data into a unified decision engine. Instead of routing every transaction through the same checks, orchestration systems can apply stronger verification only when risk indicators justify it. That improves approval rates while reducing unnecessary friction.

The model also allows organizations to test and refine fraud strategies quickly. Businesses can experiment with different security tools, adjust rules across channels and analyze performance in real time. This flexibility has become increasingly valuable as fraud tactics evolve rapidly across digital wallets, instant payments and embedded commerce.

The report notes that fraud prevention is expanding beyond the moment of payment. Modern strategies track risk signals throughout the entire customer lifecycle. That includes onboarding, account changes, transaction monitoring and dispute management after the purchase is complete.

For merchants navigating a fast-changing payments landscape, the takeaway is clear. Fraud prevention is becoming an integrated discipline rather than a standalone function. Coordinated defenses allow businesses to protect customers, preserve revenue and keep digital commerce moving.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.

Findings in the March edition of the PYMNTS Intelligence “Share of Wallet: Amazon vs. Walmart” report reveal that modern consumer is increasingly no longer one coherent decision-maker. Instead, shoppers are two psychologically distinct selves: a disciplined survival optimizer, and an aspirational reward-seeker.

In this emerging landscape, retailers like Amazon and Walmart may no longer be fighting the same war. Total share of wallet as a concept is increasingly bifurcating distinctly into the two perennial retail categories of essentials and discretionary spending. The result is not a zero-sum retail contest, but a segmentation of dominance.

The report’s findings frame this as a “consumer wallet reset,” a structural reallocation of spending driven by macroeconomic pressure, changing consumption patterns and the maturation of digital commerce.

Retail’s New Balance of Power

Consumers are not choosing between Amazon and Walmart. They are choosing both, and relying on each for a specific purpose. The PYMNTS Intelligence report found that Amazon is the go-to for discretionary purchases, while Walmart anchors essential spending.

Amazon’s ability to convert browsing into buying through one-click checkout, personalized recommendations and near-instant fulfillment makes it uniquely suited to capture discretionary spend. When consumers decide to indulge, Amazon is increasingly the default interface.

The report’s findings show that, by the fourth quarter of 2025, Amazon captured a record 11.1% of total U.S. retail spending, with outsized dominance in categories like hobby goods (35% share), electronics (32%), and apparel and furniture, where its share has more than doubled over time.

Yet Amazon’s dominance has limits. In grocery, a category defined by frequency, perishability and price sensitivity, its share remains a modest 3%. There, and across other gaps, is where Walmart is focusing.

If Amazon has mastered discretionary spending, Walmart has perfected the opposite: the steady, unglamorous capture of essentials. The retailer’s share of overall retail spending has remained remarkably stable over time, a reflection of its entrenched role in everyday consumption.

Walmart’s strength is most pronounced in grocery, where its share of food and beverage spending has increased by 13% since 2019.

But that strength comes with a trade-off. As Walmart has deepened its hold on essentials, it has lost ground in discretionary categories like clothing, electronics and hobby goods, where growth is faster and margins are often higher.

Read the report: Consumer Wallet Reset: How Amazon Wins Discretionary Spend and Walmart Holds Necessities

The modern consumer may feel conflicted, but their behavior is increasingly patterned. Survival mode and splurge mode are no longer anomalies; they are the defining rhythms of contemporary consumption.

Amazon wins when consumers want. Walmart wins when they need.

For the broader retail sector, this bifurcation presents both a challenge and a constraint. Companies that cannot clearly position themselves within either survival mode or splurge mode risk becoming less relevant. The traditional “middle” retailer offering a mix of value and aspiration without excelling at either may become a model that is increasingly difficult to sustain.

The deeper implication of this shift may also be that retail is evolving from a collection of channels into a set of behavioral platforms. Walmart and Amazon are not just destinations; they are becoming default responses to different types of consumer needs.

When a household considers how to restock the pantry, the answer is increasingly predetermined. When an individual feels the impulse to treat themselves, the pathway is similarly preconfigured.

Ultimately, in a world where consumers are more constrained, more selective and more digitally enabled, the battle for the wallet is no longer singular. It is becoming segmented, strategic and increasingly asymmetric.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.

There’s an innovation race growing over digital identity. On one hand are banks and merchants that have traditionally been reliant on tools like passwords, one-time passcodes, biometric selfies and voice checks to power online authentication.

On the other are fraudsters using artificial intelligence (AI) to replicate those signals at scale through deepfakes, synthetic identities and cloned voices.

“AI is great. It’s exciting, it’s changing commerce, and we’re all embracing it,” James Mirfin, senior vice president, Global Head of Risk and Security Intelligence Solutions at Visa, told PYMNTS  during a discussion for the March edition of the What’s Next In Payments (WNIP) series, “How Will AI Change Identity?”

“But the criminals and the bad actors have been using [AI] probably faster than some of the businesses that are trying to defend against it,” he said.

The result is that commerce is now entering an AI identity era, where trust must be established not just through credentials but through behavioral intelligence, cryptographic tokens and ecosystem-wide cooperation.

After all, the same technologies that are driving innovation are also expanding the attack surface.

How AI Is Forcing Payments to Reinvent Identity Verification

For years, financial institutions have invested heavily in identity verification tools designed to reduce fraud while maintaining a seamless user experience. These systems include document authentication, facial recognition with liveness detection and voice biometrics that allow customers to access accounts using spoken phrases.

However, the industry is seeing where the technology is advancing and improving existing defenses for fraud protection: Synthetic identities can now be generated in seconds, while AI-driven voice models can replicate a person’s speech patterns with alarming accuracy. The result is a growing sense that static authentication methods, no matter how sophisticated, may no longer be sufficient on their own — a layered approach is needed, with behavioral identity included in the mix. Behavioral identity looks at how the person acts over time, instead of asking who someone claims to be.

Mirfin said there’s a danger that when you’re building these defenses, you focus on one and think that’s the weakest link or most vulnerable. However, the reality is that companies have to be vigilant about all of these: fake voices, fake faces, fake behavior.

The result is a reframe of the very architecture of fraud detection, which has relied heavily on verifying identity at a single moment, such as when a user logs in or creates an account. As AI grows more sophisticated, that approach is giving way to continuous monitoring that evaluates how someone behaves over time.

For large enterprises with dedicated fraud teams, adapting to this new landscape is already underway. But smaller merchants often lack the resources to build advanced security systems on their own.

Ensuring Security Across the Entire User Journey

The challenge will only intensify as “agentic commerce” emerges, where AI assistants and software agents begin shopping and transacting online on behalf of consumers. In that world, merchants and payment networks will need new ways to distinguish legitimate automated activity from malicious AI agents.

“If you go back 18 months or two years, most merchants would say: ‘Bot? Stop,’” Mirfin said. “Now you’ve got good bot behavior interacting with your website, and that changes the game.”

He added, “If a consumer chooses to use an agent to shop for them, the merchant needs to be ready to accept that and recognize that interaction.”

To that end, Visa and other industry players are developing standards to help merchants and consumers trust in agent-driven transactions. One example is the introduction of “trusted agent” protocols designed to confirm that an AI acting on behalf of a consumer has legitimate authorization.

The core function of payment networks, Mirfin noted, has always been establishing trust between parties that do not know each other.

“The one that we’re spending time looking at, and it’s probably harder to detect, is fake behavior. These are bots that start to mimic human behavior or human interactions,” he said.

These illicit bot systems look less like traditional fraud and more like authentic customers navigating a website due to their capability for imitating the data-driven rhythms of human browsing patterns, typing speeds and purchasing habits.

Mirfin compared the concept of defending against these next-generation software threats to an old-school bank teller assessing customers in person.

“If you are sitting in a café or restaurant, people’s behavior typically is fairly similar,” he said. “But you can spot someone that looks a bit nervous or twitchy. It’s the same in banking. Historically, bank tellers were looking for anomalous behavior.”

In the digital world, those anomalous signals come from, amongst other things, transaction patterns, device data, and location information. When analyzed together, they form a behavioral fingerprint that is far harder for fraudsters to replicate consistently.

“You can’t just focus on account creation or setup,” Mirfin said. “It’s about identifying good behavior and good activity over time.”

What Today’s Landscape Reveals About the Next Frontier

Another foundational technology gaining renewed relevance in this AI-driven environment is tokenization.

“There’s no reason for raw credentials or identity data to move around the internet in plain form anymore,” Mirfin said. “Tokens can do more than protect payment information. They can define limits, parameters and permissions around a transaction.”

Yet technology alone may not be enough. One of the biggest structural challenges facing banks, Mirfin argued, lies inside the organizations themselves.

Fraud prevention, cybersecurity and identity verification are often handled by separate teams within financial institutions, each operating with its own tools and priorities. In an AI-driven threat environment, those silos could become a vulnerability. Gartner recently predicted structural convergence, forecasting that by 2031, 50 percent of large financial institutions will consolidate online fraud prevention, identity, and cybersecurity responsibilities under teams reporting to the CISO, driven by the identity-centric nature of modern fraud.

“Traditionally you’ve had cyber teams, fraud teams and identity teams operating separately,” Mirfin said. “I’d love to see the industry … [bring] those groups together.”

Better collaboration across those domains, he said, could help financial institutions stay ahead of rapidly evolving threats while also reducing friction for consumers.

“We all shop every day,” Mirfin said. “We want to buy things in the easiest way possible, but we also want to know we’re protected.”