Fraud Intelligence in Acquirers: Trends for 2026

Table of Contents

Acquirers face new fraud performance pressures by 2026

Measurable Fraud Outcomes in 2026

  • What changed by 2026: merchant RFPs increasingly ask for measurable fraud outcomes (especially false positives and chargeback operations), not just processing price and coverage.
  • Why it matters commercially: false declines are now framed as a larger economic drag than fraud losses in aggregate, so “approval lift with controlled risk” becomes a board-level metric for many merchants.
  • What changed operationally: Visa’s April 2026 VAMP threshold shift (220 → 150) increases the urgency of portfolio-wide monitoring, not just merchant-by-merchant remediation.

The Importance of Fraud Performance Metrics in Merchant Selection

Fraud prevention is no longer a back-office function that merchants tolerate as part of payments acceptance. By 2026, it is becoming a visible, commercial attribute of an acquiring relationship—measured, compared, and written directly into procurement processes.

Merchants are increasingly evaluating acquirers on fraud performance metrics. In practical terms, that means RFPs (Requests for Proposal) are shifting: false positive rates and chargeback handling are now as important as price. This is a meaningful change in power dynamics. Historically, acquirers competed primarily on commercial terms, coverage, and operational reliability. Now, merchants are asking a different question: Will your risk stack help me approve more good customers while keeping disputes and fraud under control?

Two metrics sit at the centre of this shift:

  • False positives (legitimate transactions incorrectly declined), which directly reduce conversion and customer lifetime value.
  • Chargeback handling, which affects operational burden, cashflow, and the merchant’s ability to defend revenue.

For acquirers, the implication is strategic: fraud performance becomes part of the sales narrative and part of the retention strategy. A portfolio that looks “clean” on paper is no longer enough; merchants want evidence that the acquirer can manage fraud and disputes without choking legitimate demand. As financial crime evolves, this is why fraud prevention is moving from a cost centre toward a competitive differentiator—especially in acquiring, where merchants can switch providers if outcomes disappoint.

Fraud Performance Evaluation Priorities
When merchants compare acquirers in an RFP, fraud performance usually lands in three buckets:
1) Approval performance (growth)

  • What they ask: approval rate by channel/region, false positive rate, “good customer” recovery flows
  • What it signals: whether the acquirer’s controls protect conversion, not just losses

2) Disputes & chargebacks (operational burden)

  • What they ask: dispute intake SLAs, representment support, reason-code analytics, win-rate reporting
  • What it signals: whether the acquirer reduces workload and stabilises cashflow

3) Portfolio governance (risk & accountability)

  • What they ask: monitoring cadence, escalation triggers, scheme-program readiness (e.g., VAMP), auditability of decisions
  • What it signals: whether the acquirer can keep portfolio ratios under control without sudden, disruptive clampdowns

A practical way to respond as an acquirer: pair each promised metric with (a) how it’s measured, (b) the control levers you’ll use, and (c) the reporting cadence the merchant will actually receive.

Projected Merchant Losses Due to False Positives in 2026

False positives are often discussed as a tuning problem—thresholds, rules, model calibration. But the 2026 outlook frames them as something bigger: a macroeconomic drag on digital commerce, and a commercial risk for acquirers whose merchants feel the pain.

The Merchant Risk Council found that, in 2026, around two-thirds of merchants (65%) estimate their false positive rate on e-commerce orders to be between 2% and 10%. That range is wide, but the message is consistent: many merchants believe a meaningful share of attempted purchases are being blocked unnecessarily.

The projected financial impact is stark. Global merchant losses due to false positives are expected to exceed $231 billion in 2026, while actual card fraud losses are expected to reach $39.6 billion. The comparison matters: it suggests that the industry can lose far more money by being overly aggressive than it loses to fraud itself.

2026 metric (merchant perspective) Figure What it implies for acquirers
Merchants estimating false positive rate between 2%–10% (e-commerce orders) ~65% Many merchants believe declines are materially suppressing revenue; acquirers will be asked to show “approval lift” plans.
Projected global merchant losses due to false positives >$231B Overly aggressive controls can be more expensive than fraud at the macro level; precision becomes a commercial differentiator.
Projected actual card fraud losses $39.6B Fraud loss reduction still matters, but merchants may prioritise balanced outcomes over “maximum blocking.”

For acquirers, this creates a delicate balancing act:

  • If controls are too loose, fraud and disputes rise—bringing scheme scrutiny and liability exposure.
  • If controls are too strict, approval rates fall—merchants lose revenue and may blame the acquirer’s risk posture.

This is why “stopping fraud” is no longer the only objective. The new target is precision: preventing real fraud while enabling legitimate transactions. In commercial terms, acquirers that can demonstrate lower false positives (without increasing fraud) can position fraud intelligence as a revenue enabler for merchants, not just a protective layer.

Impact of Visa Acquirer Monitoring Program on Fraud Accountability

Scheme monitoring has always influenced acquiring behaviour, but Visa’s Acquirer Monitoring Program (VAMP) update in 2026 raises the stakes by tightening the definition of acceptable portfolio performance.

The key change: 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. This is not a minor adjustment. It signals a stricter tolerance for elevated fraud and dispute levels across an acquirer’s book of business.

The practical effect is a fundamental shift in how payment risk is managed. Instead of treating fraud and disputes as issues to be handled merchant-by-merchant, VAMP pushes acquirers toward portfolio accountability. A handful of high-risk merchants can affect the acquirer’s overall ratio, increasing pressure to:

  • monitor fraud and disputes continuously across the portfolio,
  • intervene earlier when ratios trend upward,
  • and ensure merchants have effective controls and processes.

Another crucial point is liability. Acquirers bear the ultimate liability when merchants can’t absorb losses. Under tighter monitoring thresholds, that liability becomes more immediate and more operational: it can influence underwriting, merchant acceptance decisions, and the intensity of ongoing oversight.

In this environment, fraud intelligence becomes more than a defensive capability. It becomes a compliance and governance tool—one that helps acquirers understand where fraud and disputes are coming from, how they cluster across merchants, and what actions can reduce ratio pressure before it becomes a program-level problem.

Managing VAMP Operating Pressure
How the VAMP ratio change tends to translate into day-to-day operating pressure:
1) Define the numerator and denominator consistently

  • Numerator: combined fraud + dispute counts
  • Denominator: total settled Visa transactions

Checkpoint: if teams use different counting windows or dispute definitions, internal reporting won’t match scheme outcomes.
2) Move from “merchant exceptions” to portfolio thresholds

  • A few outliers can move the portfolio ratio, so monitoring needs to surface concentration (which merchants, which channels, which BIN/region patterns).

Checkpoint: if reporting only shows portfolio averages, you’ll miss the merchants driving the ratio.
3) Set early-warning triggers before the program threshold

  • Create internal alert bands below 150 so remediation starts while there’s still room to correct.

Checkpoint: if alerts trigger only at/near 150, actions become abrupt (sudden rule-tightening, forced merchant changes) and can spike false positives.
4) Tie remediation to specific levers

  • Examples: step-up authentication, rule tuning, model thresholds, merchant operational fixes (fulfilment proof, descriptor hygiene), dispute workflow changes.

Checkpoint: if remediation is “more declines,” you may reduce fraud counts but increase disputes and revenue loss.
5) Close the loop with merchant-facing reporting

  • Provide merchants with the same drivers you use internally (top reason codes, top fraud vectors, approval impact).

Checkpoint: if merchants can’t see what changed, they can’t operationalise fixes—and they’ll attribute outcomes to the acquirer.

AI Technologies Transforming Fraud Prevention Strategies

The “good news,” as the industry frames it, is that acquirers are not facing these pressures empty-handed. Several technologies and strategies are increasingly positioned as the path to modern fraud management—particularly where the goal is both risk reduction and better approvals.

AI enables real-time fraud prevention across all channels and payment types. That real-time element is central: fraud decisions must be made at authorization speed, and merchants expect minimal friction. AI and machine learning are used to score transactions quickly, helping acquirers and their merchants respond to evolving attack patterns without relying solely on static rules.

Alongside AI, Digital ID is highlighted as a way to build a more accurate picture of customers. The promise is improved confidence in identity and behaviour signals, which can reduce unnecessary declines—especially in e-commerce contexts where traditional signals can be thin or noisy.

A third pillar is network intelligence. Fraud rarely stays confined to one merchant. Attackers reuse infrastructure, identities, and tactics across multiple targets. Network-level signals help acquirers see patterns that a single merchant cannot, improving detection of coordinated activity and reducing repeated losses.

Taken together, these technologies support a shift from reactive fraud operations to proactive risk management. For acquirers, the strategic question becomes: how to deploy these capabilities in a way that reduces compliance risk (including scheme monitoring exposure) while also improving merchant outcomes like approval rates and customer experience.

Signals Across the Decision Lifecycle
Implementation checklist: where each signal typically plugs into decisioning

  • Authorization-time scoring (milliseconds)
  • AI/ML risk score
  • Device + behavioural signals (session velocity, interaction patterns)
  • Network intelligence flags (known attack infrastructure, cross-merchant patterns)

Checkpoint: measure approval lift vs fraud/dispute movement after each threshold change.

  • Step-up / friction controls (only when needed)
  • Digital ID confidence signals
  • Adaptive authentication triggers

Checkpoint: ensure step-up is targeted; broad step-up can reduce fraud but also depress conversion.

  • Post-transaction operations (minutes to days)
  • Case management automation (queueing, enrichment, prioritisation)
  • Dispute/chargeback analytics (reason-code drivers, representment readiness)

Checkpoint: track whether automation reduces backlog without increasing missed fraud.

  • Portfolio governance (weekly/monthly)
  • Merchant segmentation (vertical, geo, channel)
  • Drift monitoring (model performance, new fraud MO)
  • Reporting aligned to scheme/portfolio ratios

Checkpoint: if portfolio ratios improve but merchant approval drops, you’ve shifted cost—not solved it.

Commercialization of Fraud Intelligence by Acquirers

Acquirers are increasingly treating fraud intelligence as a product—something that can strengthen merchant relationships, improve portfolio performance, and create a differentiated value-added service proposition. The commercial logic is straightforward: if merchants are evaluating fraud performance in RFPs, then fraud capabilities can justify premium positioning and reduce churn.

This commercialization trend is also supported by broader market momentum. The fraud intelligence sharing market is projected to grow from $12.85B in 2025 to $70.75B by 2035 (CAGR 18.6%), reflecting rising demand for shared signals and intelligence-driven controls. In parallel, the AI in fraud management market is projected to reach $95.11B by 2036, underscoring how central AI has become to fraud strategy. (These are market projections from industry research, so treat them as directional indicators rather than precise forecasts.)

Commercialization typically takes two forms:

  1. Packaging capabilities—turning fraud tools into tiered offerings that merchants can buy.
  2. Proving outcomes—showing measurable improvements in approvals, fraud loss reduction, and operational burden.

The pressure from programs like VAMP accelerates this shift. When acquirers are held accountable for fraud and disputes, investing in better intelligence is not just a merchant service—it is a way to protect the acquirer’s own risk position. The result is a market where fraud prevention is increasingly sold as both protection and performance.

Commercialisation Flow for Acquirers
A practical commercialisation flow many acquirers follow (and where it can fail):
1) Package the capability

  • Define what’s included (real-time scoring, network signals, case tooling, reporting) and what’s optional.

Checkpoint: if the package is “features,” merchants will compare you on price; if it’s “outcomes,” you can defend value.
2) Baseline the merchant

  • Capture starting metrics: approval rate, false positive estimate/proxy, fraud rate, dispute rate, manual review rate.

Checkpoint: without a baseline, you can’t prove improvement—and you’ll argue about measurement later.
3) Pilot with tight measurement

  • Run A/B or phased rollout by channel/region/traffic slice.

Checkpoint: pilots that change too many levers at once can’t explain what drove results.
4) Operationalise the controls

  • Define who tunes models/rules, how often, and what triggers changes (including portfolio ratio pressure).

Checkpoint: if tuning is ad hoc, performance will drift and merchants will feel “random declines.”
5) Report outcomes in merchant language

  • Translate risk metrics into commercial impact (saved disputes, approval lift, reduced review workload).

Checkpoint: if reporting is purely technical, procurement won’t credit the value.
6) Price and renew on delivered value

  • Tie tiers to measurable service levels (report cadence, response SLAs, coverage) and demonstrated performance.

Checkpoint: if renewals aren’t linked to outcomes, the service becomes a cost line item again.

Bundling Fraud Intelligence as a Premium Service

One of the clearest monetization strategies is bundling advanced fraud intelligence into premium packages for merchants. The value proposition is not simply “we block fraud,” but “we help you grow safely.”

These bundles commonly include real-time transaction monitoring, where AI/ML scores transactions in milliseconds to support instant decisions. The commercial benefit is twofold: fewer fraudulent approvals (reducing disputes and losses) and fewer false declines (protecting conversion).

Another element is portfolio-level intelligence—risk analytics applied across the acquirer’s merchant base, not just within one merchant’s data. This matters because coordinated attacks often hit multiple merchants in the same window. Portfolio visibility can help identify emerging patterns earlier than merchant-only tools.

Bundled offerings may also include customised risk scoring tuned by vertical, geography, and channel. The underlying idea is that fraud patterns differ across segments, and merchants want controls that reflect their specific risk reality rather than generic thresholds.

For acquirers, bundling creates a commercial lever: it supports premium pricing and deeper integration into merchant operations. For merchants, it reframes fraud tooling as a service that can improve approval rates and reduce operational headaches—especially when false positives and chargeback handling are now procurement priorities.

Leveraging Network and Consortium Intelligence

Network and consortium intelligence is increasingly positioned as the differentiator that individual merchants cannot replicate on their own. Where a merchant sees only its own traffic, a network sees patterns across institutions and portfolios.

A key use case is early warning: shared signals can help detect fraud rings, mule activity, or synthetic identity patterns before they fully manifest in a single merchant’s data. In a world where attackers scale quickly, earlier detection can be the difference between a contained incident and a portfolio-wide spike in disputes.

It also supports ecosystem-level insights. Broader visibility into tactics and infrastructure can reduce repeated attacks and improve the quality of fraud decisions—especially when combined with AI models that can adapt to new patterns.

Commercially, acquirers can translate this into a stronger merchant proposition: “You’re not just buying our tools; you’re benefiting from intelligence gathered across a wider ecosystem.” That message aligns with the industry’s move toward unified platforms that integrate real-time scoring, case management, and shared signals.

Operational Efficiency Gains from Advanced Fraud Management

Fraud intelligence is not only about better decisions; it is also about running the fraud function at a lower unit cost. As fraud volumes and dispute complexity rise, manual operations become expensive—and they scale poorly.

Advanced fraud management emphasizes automation and workflow integration. Automated case management and alerting can streamline investigations, reducing analyst workload and accelerating response times. The operational promise is that teams can handle more volume without proportional headcount growth, which directly protects margins.

Efficiency also shows up in merchant-facing processes. Real-time intelligence can support faster onboarding and approvals, particularly in fast-moving e-commerce segments where merchants value speed. When risk decisions are data-driven and automated, acquirers can reduce friction in underwriting and ongoing monitoring—while still maintaining control over portfolio risk.

Crucially, operational efficiency ties back to false positives. Precision scoring that reduces unnecessary declines is not just a revenue win for merchants; it also reduces downstream operational costs associated with customer complaints, manual reviews, and dispute handling.

The industry trend is toward unified, AI-native platforms that cover the end-to-end workflow—from real-time monitoring to case management and regulatory documentation. This consolidation reduces “integration debt” from fragmented point solutions and supports more consistent, defensible decisioning. In a tighter accountability environment shaped by programs like VAMP, efficiency and defensibility become part of the same operational mandate.

Measuring Operational Efficiency Gains
Operational KPIs that make “efficiency gains” measurable (and easier to prove in merchant reviews):

  • Manual review rate: % of transactions routed to human review (target: down without increasing fraud/disputes)
  • Time-to-decision: median time from alert to disposition (target: minutes/hours, not days)
  • Alert-to-case conversion: % of alerts that become cases (target: down as alert quality improves)
  • Analyst throughput: cases closed per analyst per day/week (target: up without quality drop)
  • Dispute workload: disputes per 1,000 transactions and average handling time (target: down)
  • Representment readiness: % of disputes with complete evidence pack on first pass (target: up)

These metrics also help separate “we declined more” from “we got more precise,” because they show whether the operation improved alongside risk outcomes.

The Future of Fraud Intelligence Commercialisation

By 2026, the direction of travel is clear: fraud intelligence is becoming a commercial asset for acquirers, not merely a protective control. Merchant expectations are rising, scheme accountability is tightening, and the economics of false positives are too large to ignore.

The landscape is evolving along three connected axes:

  • Performance transparency: merchants increasingly compare acquirers on fraud outcomes.
  • Portfolio accountability: monitoring programs push acquirers to manage fraud and disputes as a portfolio problem.
  • Technology acceleration: AI, Digital ID, and network intelligence raise the ceiling on what “good” looks like in fraud decisioning.

In that environment, commercialization is less about selling add-ons and more about embedding fraud intelligence into the core acquiring proposition—positioning it as a driver of approvals, customer experience, and sustainable growth.

Balancing Fraud Intelligence Priorities
Commercialising fraud intelligence: the trade-offs acquirers typically have to manage

  • Approval lift vs loss/dispute volatility
  • Pushing approvals up can expose weak spots; the goal is controlled lift with tight monitoring of fraud + disputes, not “approve more at any cost.”
  • Portfolio standardisation vs merchant-specific tuning
  • Standard controls scale better, but vertical/geography differences can make one-size-fits-all thresholds expensive in false positives.
  • Sharing/consuming network intelligence vs operational complexity
  • Broader signals can improve early warning, but they add integration, governance, and interpretation work.
  • Model power vs explainability
  • More complex models can be more accurate, but merchants (and internal governance) often need clear reasons for declines and clear levers for remediation.
  • Monetisation vs trust
  • Charging for intelligence can work when outcomes are proven; if value isn’t visible in reporting, merchants will treat it as a fee—not a partnership.

Strategic Partnerships and Collaborations

Commercialisation also depends on collaboration—both with technology providers and across intelligence-sharing ecosystems. As the fraud intelligence sharing market expands, acquirers are incentivized to participate in networks that improve detection and shorten response cycles.

Partnerships with fraud technology vendors can accelerate access to unified platforms, real-time scoring, and automated workflows. Meanwhile, consortium and network intelligence arrangements can provide the broader visibility that individual merchants and single institutions lack.

The strategic outcome is differentiation: acquirers that combine strong internal risk operations with external intelligence and modern AI capabilities can offer merchants a clearer promise—better approvals with controlled fraud and disputes—while also protecting their own portfolio ratios under tighter scheme monitoring.

This perspective is shaped by Martin Weidemann’s work building and operating payments and digital businesses in regulated, multi-stakeholder environments across the Americas, where chargebacks, disputes, and fraud controls directly influence approval rates, unit economics, and merchant retention.

Figures described as “projected” or “expected” reflect publicly available estimates at the time of writing and may change as new information emerges. Program thresholds and monitoring details can also evolve with future updates. Treat the metrics and checkpoints as practical guides for structuring measurement and merchant discussions, not as fixed commitments.

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