Fraud Intelligence in Acquirers: Trends for 2026

Table of Contents

• 1. Acquirers face pressure on fraud performance metrics
• 2. The Evolving Role of Acquirers in Fraud Prevention
• 3. Impact of False Positives on Merchant Losses
• 4. Projected Financial Losses Due to Fraud in 2026
• 5. Visa’s Acquirer Monitoring Program and Accountability
• 6. Technological Innovations in Fraud Prevention
• 6.1 AI-Driven Solutions
• 6.2 Digital ID and Network Intelligence
• 7. Merchant Expectations and the Shift in Acquirer Evaluation
• 8. The Future of Fraud Intelligence Commercialisation
• 8.1 Embracing Technological Advancements
• 8.2 Building Stronger Merchant Relationships

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Acquirers face pressure on fraud performance metrics

• Merchants are increasingly selecting acquirers based on fraud performance metrics—not just price.
• False positives are a major economic problem: global merchant losses are expected to exceed $231B in 2026.
• By comparison, actual card fraud losses are projected at $39.6B in 2026, sharpening focus on precision.
• Visa’s VAMP update in April 2026 lowered the ratio threshold from 220 to 150, raising accountability for acquirers.

Key Metrics for Acquirer Selection
Metrics that now matter in acquirer selection (as reflected in merchant RFPs and network programs):

• Approval performance: approval rate and “good customer” pass-through (often proxied by false-positive rate)
• Disputes/chargebacks: dispute volume, representment outcomes, and operational handling speed
• Portfolio risk: combined fraud + dispute ratios at scale (e.g., VAMP-style thresholds)
• Transparency: merchant-facing reporting that makes these metrics auditable over time

Key terms used in this article (for consistency):

• False positive: a legitimate transaction incorrectly declined or flagged as fraud.
• Disputes / chargebacks: customer-initiated disputes that can result in chargebacks and operational cost.
• VAMP ratio: combined fraud and dispute counts divided by total settled Visa transactions.

The Evolving Role of Acquirers in Fraud Prevention

For years, fraud prevention in acquiring was often treated as a necessary cost of doing business: rules, manual reviews, and a posture that leaned reactive—handle what breaks, absorb what can’t be avoided, and keep processing volumes growing. In 2026, that framing is breaking down.

The shift is being driven by two forces moving at the same time. First, financial crime is evolving quickly, with fraudsters using automation and AI-enabled tactics to scale attacks across digital commerce and new payment rails. Second, merchants are changing what they demand from their acquirers: not only authorization and settlement, but active partnership in managing risk across the portfolio.

That partnership is becoming more explicit in commercial processes. In RFPs, merchants are now weighing false positive rates and chargeback handling alongside pricing. This changes the acquirer’s job: it’s no longer enough to “block bad transactions.” The acquirer must help merchants approve more good ones while keeping fraud and disputes within acceptable thresholds.

The liability structure reinforces this evolution. Acquirers ultimately bear the risk when merchants can’t absorb losses, which makes portfolio-wide performance—not just individual merchant outcomes—a strategic priority. In practice, fraud prevention is moving from a back-office control function into a product capability that can strengthen merchant relationships, improve portfolio performance, and differentiate an acquirer in a competitive market.

Risk Partnership Lifecycle Overview
A practical “risk partnership” lifecycle (where acquirers can productise fraud intelligence):
1) Merchant onboarding: underwriting + KYB/KYC signals → set initial risk tier and controls
2) Transaction decisioning: real-time scoring + rules/AI → approve/decline/step-up with minimal friction
3) Monitoring & tuning: track fraud, disputes, and false positives → adjust thresholds/models by segment
4) Disputes/chargebacks ops: representment workflows + reason-code analytics → reduce dispute leakage
5) Portfolio governance: roll up merchant performance → intervene early where ratios trend toward network thresholds
Checkpoints: if approvals drop while fraud stays flat, investigate false positives; if disputes rise without fraud rising, investigate customer experience/descriptor/fulfilment and first-party misuse patterns.

Impact of False Positives on Merchant Losses

False positives—legitimate transactions incorrectly declined or flagged as fraud—have become one of the most expensive “silent failures” in digital commerce. The Merchant Risk Council found that in 2026, around 65% of merchants estimate their false positive rate on e-commerce orders falls between 2% and 10%. Even at the low end of that range, the cumulative impact is enormous when applied to global e-commerce volumes.

The headline number is stark: global merchant losses due to false positives are expected to exceed $231 billion in 2026. That figure reframes the conversation, because it suggests that the economic damage from overly aggressive fraud controls can dwarf the losses from fraud itself.

This is not just a merchant problem. False positives directly affect acquirers’ economics and competitiveness. When legitimate customers are declined, merchants lose revenue and may blame the payments stack—often the acquirer—especially if declines appear inconsistent across channels or geographies. Over time, that erodes trust and increases churn risk, particularly as merchants become more sophisticated buyers who compare providers on measurable performance.

False positives also create operational drag. More declines and more customer complaints can translate into more manual reviews, more support tickets, and more disputes—costs that sit across both merchant and acquirer operations. The strategic implication for acquirers is clear: fraud prevention must be tuned for precision, not just strictness. Stopping fraud matters, but enabling legitimate transactions is now a core part of “fraud performance.”

Projected Financial Losses Due to Fraud in 2026

In 2026, the payments industry is confronting two parallel loss curves: the direct cost of fraud, and the indirect cost of preventing fraud poorly. On the direct side, actual card fraud losses are projected to reach $39.6 billion globally. That is a large number by any standard, and it continues to justify investment in detection, monitoring, and dispute management.

But the comparison that is reshaping priorities is the gap between that $39.6B and the projected $231B+ in merchant losses attributed to false positives. Even without treating these categories as perfectly comparable, the magnitude difference highlights a practical truth: if fraud controls are too blunt, the “cure” can become more expensive than the disease.

This matters for acquirers because fraud losses and dispute volumes are not isolated to individual merchants; they can accumulate into portfolio-level risk. As fraud tactics evolve—spanning digital onboarding fraud, authorized payment fraud, and AI-driven financial crime—acquirers face pressure to manage risk across channels and payment types, not just card-not-present transactions in a single vertical.

Merchants, meanwhile, are dealing with a diverse threat environment. The Merchant Risk Council reports that merchants experienced an average of 3.7 different fraud attacks in 2025, underscoring that fraud is not one problem but many, often happening at once. That diversity increases the chance that simplistic controls will either miss real fraud or over-block legitimate customers.

The 2026 outlook, then, is less about choosing between fraud prevention and growth, and more about building systems that can do both: reduce fraud losses while minimizing friction and preserving approvals.

Separating Fraud and False Positives
How to interpret “fraud losses” vs “false-positive losses” without mixing categories:

• Direct fraud losses: fraud write-offs, recovery/investigation costs, and dispute/chargeback operational costs tied to confirmed fraud.
• Indirect prevention losses: revenue lost from unnecessary declines (false positives), customer churn, and added support/manual-review workload.
• Portfolio effect (acquirer-specific): even if a single merchant can absorb losses, ratios can accumulate across the book and trigger network scrutiny.

Use this lens when comparing big numbers like $39.6B (direct card fraud) vs $231B+ (false-positive impact): they describe different mechanisms, but both hit merchant economics and acquirer competitiveness.

Visa’s Acquirer Monitoring Program and Accountability

Visa’s Acquirer Monitoring Program (VAMP) is one of the clearest signals that accountability is tightening at the acquirer level. The April 2026 update lowered the VAMP ratio—defined as combined fraud and dispute counts divided by total settled Visa transactions—from 220 to 150.

That change is not a minor calibration. It effectively raises the standard for portfolio-wide performance and makes it harder for acquirers to treat fraud and disputes as issues that can be managed merchant-by-merchant without broader consequences. When the threshold tightens, the margin for “acceptable” underperformance shrinks, and the cost of unmanaged risk rises.

In practical terms, this pushes acquirers toward more consistent, intelligence-led risk management across their merchant base. If an acquirer’s portfolio includes segments with elevated disputes or fraud, the combined effect can threaten compliance posture even if many merchants are performing well. The program design therefore encourages earlier intervention: better onboarding controls, stronger monitoring, and more effective dispute and chargeback handling.

It also changes internal incentives. Fraud prevention becomes tied more directly to commercial outcomes—because compliance risk can translate into operational restrictions, reputational damage, or increased scrutiny. And because merchants are already evaluating acquirers on false positives and chargeback handling, the same capabilities that help an acquirer stay within VAMP expectations can also become part of a differentiated value proposition.

The broader takeaway is that network rules are reinforcing what the market is already demanding: measurable fraud and dispute performance, managed at scale.

VAMP Threshold Shift Impact
April 2026 VAMP threshold change (why teams felt it immediately):

• What changed: the VAMP ratio threshold moved from 220 → 150 (Visa program update referenced in Finextra’s 2026 summary).
• Why it matters: less “buffer” for elevated fraud/disputes anywhere in the portfolio, so weak segments can affect the whole book.
• Operational implication: earlier intervention becomes the default—tighten onboarding, tune decisioning to reduce both fraud and disputes, and treat dispute ops as a risk-control lever (not just back office).

Technological Innovations in Fraud Prevention

The “good news,” as the industry frames it, is that acquirers are not facing these pressures without better tools. Several technology approaches are repeatedly highlighted as enabling a more modern fraud posture: AI for real-time prevention across channels and payment types, and Digital ID plus network intelligence to build a more accurate picture of customers.

What’s changing is not only the sophistication of detection, but the speed and scope. Fraud controls are moving away from static, batch-oriented approaches toward real-time decisioning and portfolio-level intelligence. This matters because modern fraud is fast, adaptive, and often coordinated across merchants and channels.

At the same time, innovation is not purely technical. It is also commercial. Acquirers are increasingly positioning fraud intelligence as a value-added service—something that can improve merchant outcomes (approvals, fewer chargebacks, lower operational burden) while also improving the acquirer’s portfolio performance and compliance readiness.

What “commercialising fraud intelligence” looks like in practice:

• Real-time decisioning that supports higher approvals while controlling fraud and disputes.
• Portfolio-level intelligence to spot coordinated patterns across merchants and channels.
• Precision tuning to reduce false positives, since unnecessary declines can be more costly than fraud itself.
• Merchant-facing reporting on measurable performance (e.g., false positives and chargeback handling) aligned to how RFPs are now evaluated.
• Collaborative, anonymised intelligence sharing models that improve detection without adding friction for genuine payments.

AI-Driven Solutions

AI is increasingly central to how acquirers attempt to balance two competing goals: stopping fraud and reducing friction. The promise is real-time decisioning—scoring transactions in milliseconds—so that risk controls can operate across channels and payment types without slowing commerce.

This approach is often contrasted with older models built on static rules and manual review queues. Static rules can be effective for known patterns, but they can also be brittle: they may fail when fraud tactics shift, and they can generate unnecessary declines when legitimate behavior looks “unusual” by simplistic standards. AI-driven models aim to detect subtler anomalies and adapt more quickly as patterns change.

The adoption trend is visible on the merchant side as well. The Merchant Risk Council notes that 63% of merchants are actively exploring or planning to implement agentic AI payments, reflecting broader interest in AI-enabled automation across payments operations, including fraud.

For acquirers, the operational value is also important. Automated, intelligence-led decisioning can reduce manual workload for fraud teams, especially during spikes in attack volume or seasonal peaks. That efficiency can translate into faster response times and more consistent outcomes across a merchant portfolio.

Commercially, the logic is straightforward: if AI improves approval rates while keeping fraud and disputes down, it becomes a differentiator that is difficult to replicate purely through pricing.

Digital ID and Network Intelligence

Digital ID and network intelligence address a core limitation of transaction-only fraud controls: a single transaction rarely contains enough context to confidently separate fraud from legitimate behavior. By building a more accurate picture of the customer—using identity signals and broader network patterns—acquirers can make better decisions with fewer false positives.

Network intelligence also aligns with a growing industry belief that fraud is harder to stop in isolation. Collaborative models are emerging to share anonymized fraud signals across participants, improving detection and reducing blind spots. One example is Bottomline’s Fraud Intelligence Exchange, piloted with leading banks in 2026, reflecting the push toward shared intelligence as a way to respond faster to emerging threats.

This kind of intelligence sharing is often positioned as a way to improve precision without adding friction for genuine payments. If multiple institutions can see early signals of coordinated attacks, they can adjust controls before losses spread widely.

However, these approaches also bring governance requirements. Collaborative intelligence exchanges must navigate data privacy and security constraints, and their success depends on trust, anonymization, and robust controls around how signals are shared and used.

For acquirers, the strategic value is that Digital ID and network intelligence can support both compliance readiness and merchant experience—reducing fraud while also reducing unnecessary declines.

Merchant Expectations and the Shift in Acquirer Evaluation

Merchant expectations are now reshaping the acquiring market in measurable ways. The clearest signal is procurement behavior: false positive rates and chargeback handling are now as important to merchants as price. That is a fundamental change in how acquiring services are evaluated, because it elevates fraud performance from an operational detail to a buying criterion.

This shift is rational when viewed through the loss numbers. If false positives can drive global merchant losses to that level, then merchants have a strong incentive to prioritize partners who can protect approvals. Meanwhile, merchants still need robust fraud prevention—but not at the expense of legitimate revenue.

Merchants are also operating in a complex threat environment. With an average of 3.7 different fraud attacks experienced per merchant in 2025, the “one-size-fits-all” approach becomes less credible. Different verticals, customer bases, and geographies can produce different risk profiles, and merchants increasingly expect acquirers to reflect that reality in how they manage risk and disputes.

For acquirers, this changes the commercial playbook. Fraud prevention can no longer be framed only as compliance or loss avoidance. It becomes part of the service proposition: measurable outcomes, portfolio insights, and operational support that reduce friction and improve performance.

In that context, fraud intelligence becomes a relationship tool. Acquirers that can demonstrate better approval rates, lower false positives, and stronger dispute management are positioned to win and retain merchants—even when competing against providers that try to lead with price alone.

Acquirer Fraud Performance Questions
If you’re a merchant evaluating an acquirer (or writing an RFP), the questions that surface “fraud performance” quickly:

• False positives: How do you measure FP rate, and can you break it down by channel, country, and issuer response codes?
• Approvals: What approval-rate lift have you delivered in comparable portfolios, and over what time window?
• Disputes/chargebacks: What are your dispute SLAs, representment support, and reporting by reason code?
• Tuning cadence: How often are models/rules tuned, and what triggers an out-of-cycle change (e.g., attack spikes)?
• Portfolio intelligence: Can you detect coordinated patterns across merchants, and how do you operationalise interventions?
• Merchant reporting: Do we get dashboards we can reconcile to our own order data (declines, retries, customer contacts)?
• Governance: If you use shared/network signals, what is shared, how is it anonymised, and how do you prevent misuse?

The Future of Fraud Intelligence Commercialisation

Embracing Technological Advancements

By 2026, fraud prevention is increasingly described as moving from a cost centre to a competitive differentiator—especially for acquirers. The commercialisation path follows the technology: real-time AI decisioning, Digital ID, and network intelligence are not just internal controls; they can be packaged into value-added services that improve merchant outcomes and portfolio performance.

The direction of travel is toward embedded intelligence across the merchant lifecycle—from onboarding and ongoing monitoring to dispute and chargeback handling—because portfolio-level accountability is rising and fraud tactics are evolving quickly. In that environment, the acquirers best positioned for the next phase are those that treat fraud intelligence as a core capability, not an add-on.

Building Stronger Merchant Relationships

The market signal from merchants is unambiguous: fraud performance metrics now sit alongside price in acquirer selection. That means commercial success increasingly depends on trust, transparency, and measurable results—especially around false positives and disputes.

As programs like Visa’s VAMP tighten thresholds and merchants quantify the cost of unnecessary declines, acquirers have an incentive to turn fraud intelligence into a shared operating model with merchants: aligned metrics, clearer accountability, and tools that protect both revenue and risk posture.

In 2026, the acquirer-merchant relationship is being renegotiated around one idea: the best fraud prevention is not the strictest—it’s the most precise.

Commercialising Fraud Intelligence Model
A simple model for commercialising fraud intelligence (what to package, price, and prove):
1) Package: bundle capabilities into tiers (e.g., baseline screening → real-time AI → portfolio/network intelligence → dispute ops analytics).
2) Price: align to value drivers merchants feel (approval lift, dispute reduction, operational workload reduction), not only per-transaction fees.
3) Prove ROI: agree on a before/after measurement plan (approval rate, FP rate, dispute rate, manual reviews) and review it on a fixed cadence.
4) Share signals: provide merchant-facing insights (why declines happen, where disputes originate) and—where applicable—use anonymised network signals to improve precision.
5) Operationalise: define who acts when metrics move (merchant, acquirer risk, PSP ops) so “intelligence” turns into outcomes.

Perspective: This framing reflects a payments-operations view shaped by building and scaling payment and risk workflows across the Americas, where approval rates, disputes, and fraud controls have to be managed as portfolio-level business levers—not just compliance checkboxes (Martin Weidemann, weidemann.tech).

Any 2026 figures cited (such as fraud and false-positive losses) reflect industry projections available at the time of writing and should be treated as estimates, not definitive totals. Thresholds, definitions, and monitoring rules may change, so readers should verify current ratios and requirements against the latest card network documentation. Technology and exchange examples are illustrative of operating models and are not endorsements or a recommendation of any single vendor or approach.