The Definitive 2026 Guide to Choosing an Ecommerce Fraud Prevention Platform

Last updated: April 2026  |  Reading time: ~18 minutes

Choosing a fraud prevention platform is one of the highest-stakes technology decisions an ecommerce organization makes — and one of the least well-understood. The category is crowded, the claims are largely indistinguishable, and the cost of a wrong decision compounds over time: fraud losses that continue, legitimate customers who keep getting declined, and integrations that are painful to unwind.

This guide is designed to give fraud, risk, product, finance, and commerce teams a common framework for evaluating platforms — one that goes beyond feature checklists to address the harder questions: what kind of platform architecture actually produces better outcomes, whose incentives are aligned with yours, and how to tell the difference between a vendor’s genuine capability and a well-produced demo.

What Does the Ecommerce Fraud Landscape Look Like in 2026?

Ecommerce fraud refers to unauthorized or deceptive online transactions that cause financial losses for retailers and customers. In 2026, the threat environment is more complex than at any prior point — not because fraud has become more prevalent in every category, but because the tactics have become more coordinated, more automated, and increasingly AI-assisted.

The most common fraud types facing ecommerce merchants today include:

According to the 2025 LexisNexis Risk Solutions True Cost of Fraud Study — the industry’s most comprehensive annual measurement of fraud’s financial impact — U.S. merchants incur an average all-in cost of $4.61 for every $1 of fraud, a 32% increase since 2022. That figure encompasses not just the direct fraud loss but the operational burden, customer churn, and compliance costs that compound around every incident.

The fraud attack surface has also expanded significantly beyond checkout. Criminals are now targeting identity verification at account creation, exploiting digital wallet provisioning flows, and using generative AI to automate social engineering at scale. According to the MRC/Visa 2025 Global eCommerce Payments & Fraud Report, 98% of merchants experienced some form of fraud in the prior year — making the question not whether fraud will affect your business but how well-positioned your platform is to absorb, adapt to, and minimize it.

Why Do Online Retailers Need a Dedicated Fraud Prevention Platform?

The business case for dedicated fraud prevention has historically been framed around loss reduction. That framing is incomplete — and increasingly, it is the wrong frame entirely.

The most important thing a fraud prevention platform does is not decline bad transactions. It is approve good ones. The two outcomes are inseparable, but they are not the same objective — and the distinction matters enormously when evaluating platforms. A platform optimized purely to minimize fraud losses will systematically over-decline at the margin. A platform optimized to accurately distinguish good customers from bad ones protects revenue on both sides of the decision.

The 2025 LexisNexis True Cost of Fraud Study found that fraud prevention causes customer churn at 59% of U.S. merchants, and that 36-37% of customers abandon transactions at account creation due to excessive friction. False positives — legitimate orders declined as fraudulent — are not a minor calibration issue. They are a material revenue leak that often exceeds the fraud losses they were designed to prevent.

On the investment side, generative AI adoption for fraud management reached 56% of merchants in 2025 — up from 42% the prior year — and is projected to reach nearly 80% by end of 2025, per the MRC/Visa 2025 Global eCommerce Payments & Fraud Report. The gap between organizations using advanced fraud prevention and those relying on manual or rules-based systems is widening. Forty-one percent of North American merchants still depend on manual fraud prevention processes, according to LexisNexis — a posture that is becoming increasingly untenable as attack velocity and sophistication accelerate.

What Are the Most Important Criteria When Choosing an Ecommerce Fraud Prevention Platform?

The following criteria are organized not by feature category but by the strategic questions they answer. For each criterion, the goal is not to check a box but to understand the architectural choices that produce durable performance advantages.

1. How broad and deep is the platform’s data and signal coverage?

Deep, multi-dimensional data is the foundation of fraud detection accuracy. Volume of signals matters, but so does the quality, freshness, and independence of those signals.

Leading platforms draw on a combination of the following signal categories:

• Device and browser fingerprints — hardware and software attributes that persist across sessions
• Behavioral biometrics — typing cadence, mouse movement, touch patterns, and session navigation that distinguish human behavior from automation
• Identity network data — cross-merchant intelligence on known bad actors, synthetic identities, and compromised credentials
• IP reputation and geolocation — proxy detection, VPN identification, and geographic consistency signals
• Historical transaction patterns — velocity, channel, payment method, and fulfillment behavior across prior orders
• Post-transaction signals — dispute history, return behavior, and chargeback patterns

When evaluating signal breadth, distinguish between signals a platform accesses through third-party integrations and signals it holds natively. Both have value — but native signals are available with lower latency, higher reliability, and without dependency on third-party uptime or data sharing agreements.

2. Does the platform orchestrate multiple model generations — or replace old models with new ones?

The fraud detection model landscape has evolved through several distinct generations, each solving a different problem:

The critical insight is that each model generation did not make the previous one obsolete — it added a new detection capability that the prior generation lacked. Removing older models to replace them with newer ones creates dangerous blind spots: the transactions that a velocity rule catches in milliseconds are transactions a transformer model may never flag, because they don’t exhibit the complex behavioral patterns the transformer was trained to find.

Most vendors in the fraud prevention category lead with their newest model architecture as a selling point. The sophisticated question is not “what is your newest model?” but “how do your models work together?” A platform that runs a sequential transformer while deprecating its rules engine is leaving fraud on the table. A platform that orchestrates all model generations — routing each transaction through the models best suited to catch its fraud type — produces cumulative detection coverage that no single model, however advanced, can match.

This also has direct implications for AI-generated fraud threats. Tools like FraudGPT enable fraudsters to generate synthetic identities, realistic behavioral mimicry, and coordinated attack patterns at industrial scale. Detecting these threats requires understanding context, relationships, and behavioral sequences simultaneously — which is precisely why orchestrated multi-model architectures outperform any single model approach against AI-assisted fraud.

Key questions when evaluating ML architecture:

• Does the platform maintain and actively use multiple model types simultaneously, or has it deprecated older models in favor of its current generation?
• How does the platform decide which model’s output takes precedence for a given transaction type?
• Can the platform explain which model flagged a specific transaction — and why that model was the right one to catch it?
• How does the platform’s model stack handle AI-generated fraud patterns that have no historical training equivalent?

3. How fast does the platform make risk decisions — and can it sustain that speed at peak load?

Sub-100ms decisioning is the operational standard for ecommerce fraud prevention at scale. Latency is not a technical footnote — it is a revenue variable. Checkout abandonment increases measurably with page load and response time increases, and a fraud scoring layer that adds perceptible latency to checkout is a direct conversion cost.

Global ecommerce fraud losses are projected to reach $43.6 billion by 2027, up from $33.2 billion in 2025, per the MRC/Visa 2025 Global eCommerce Payments & Fraud Report. At that loss scale, the platforms that can sustain real-time scoring under peak transaction load — not average load — will be the ones generating sustainable competitive advantage for their clients.

When evaluating latency claims, request p50, p95, and p99 latency measurements at peak transaction volume — not average volume. P99 latency is the number that affects real customer experiences during your highest-traffic, highest-fraud-risk periods. A vendor who cannot produce peak-load latency data has not stress-tested their system under conditions that matter.

4. Does the platform join signals across the full customer lifecycle, or only at the point of transaction?

One of the most underexamined criteria in platform selection is how a solution unifies data across the full customer lifecycle — not just at the moment of transaction. Most platforms claim broad data coverage, but coverage and coherence are not the same thing. A platform may ingest dozens of signals through third-party integrations while those signals remain siloed, joined at query time rather than natively unified across a customer’s history.

Why this matters: fraud does not happen at a single moment. It builds across a customer timeline — a new account with no purchase history, an address change followed by a high-value order, a return pattern that mirrors professional refund abuse. Platforms that evaluate each transaction in isolation, even with sophisticated ML, are working with an incomplete picture. Platforms that natively join signals across the lifecycle can recognize behavioral arcs before they become losses.

This criterion also directly affects the platform’s ability to say yes confidently to good customers — not just to catch fraud. A holistic view of a customer’s history supports more accurate approvals, not just more accurate declines. When evaluating platforms, ask vendors to demonstrate — not describe — how signals from account onboarding, session behavior, transaction history, dispute records, and post-fulfillment activity are connected within their data model.

• Is customer history natively stored and queryable within the platform, or assembled at runtime from external systems?
• Does the platform score based on signals from the current transaction only, or does it incorporate longitudinal behavioral context?
• Are post-transaction signals (disputes, returns, chargebacks) fed back into the risk model, or handled by a separate system?
• Can the platform identify repeat bad actors across different identities, devices, or payment instruments over time?

5. Does the platform have fraud models built specifically for your industry — or does it apply a generic model across all verticals?

A platform’s ability to detect fraud accurately is inseparable from how well it understands the context in which a transaction occurs. A generic fraud model applied across all industries will underperform in any specific one — because what counts as suspicious in airline booking looks nothing like what counts as suspicious in iGaming, retail, or financial services.

This matters for two distinct reasons:

Vertical-calibrated baselines. A model trained on travel data understands that a last-minute international booking paid with a new card is common among business travelers — not inherently suspicious. A retail model understands that velocity spikes during peak shopping periods are expected, not anomalous. A gaming model understands deposit-withdraw cycling patterns. Without vertical calibration, models either over-flag legitimate behavior or require merchants to invest significant time tuning rules to compensate.

Vertical-native signals. Some fraud indicators only exist within a specific industry context. An “impossible route” flag — two flight segments booked simultaneously from cities thousands of miles apart — is a high-confidence fraud signal in airline but irrelevant in retail. “Same device across multiple player accounts” is a primary iGaming fraud indicator. “Returns velocity by SKU category” is a retail-specific abuse pattern. Platforms that lack native vertical signal libraries require merchants to build these signals manually through custom rules — a resource-intensive process that is slow to deploy and slower to adapt.

When evaluating platforms, ask vendors to enumerate the vertical-specific signals included natively in their models — not just the verticals they serve. A vendor may claim to support retail, travel, and gaming while running the same underlying model with minor parameter adjustments. The test is whether they can name the industry-specific signals their model uses and demonstrate how those signals behave differently across verticals in their detection logic.

6. What operational tools does the platform provide for fraud teams managing daily review queues?

A platform’s detection capability is only as useful as the operational infrastructure built around it. For fraud operations teams managing hundreds or thousands of reviews per day, case management tooling is not a secondary consideration — it is the primary user experience.

Evaluate platforms on:

• Centralized case management with customizable review queues and analyst assignment
• Automated escalation rules that route cases by risk score, transaction value, or fraud type
• Audit trails and analyst collaboration tools for complex cases
• Chargeback and dispute tracking integrated into the same workflow as fraud review
• SLA management and reporting for manual review throughput

7. How does the platform support compliance with GDPR, PSD2, and other privacy regulations?

Fraud prevention platforms collect and process significant volumes of behavioral and identity data. As regulatory requirements become stricter globally, the platform’s approach to data privacy is not a legal checkbox — it is an architectural question. Verify that vendors can document support for:

How Do You Evaluate an Ecommerce Fraud Prevention Platform Step by Step?

Step 1: What fraud types and attack windows are you most exposed to?

Map the specific fraud types and attack windows most relevant to your business model, transaction mix, and customer base before engaging any vendor.

• Chart fraud types by volume and revenue impact (CNP, ATO, returns abuse, bot attacks, first-party misuse)
• Identify peak attack windows — promotional periods, new product launches, high-traffic events
• Segment by channel: web, mobile app, and marketplace transactions have distinct risk profiles
• Identify the verticals you operate in and confirm vendors have native models for those verticals

Step 2: What does success look like — and how will you measure it?

Establish concrete, measurable outcomes before vendor conversations begin. This prevents evaluation criteria from drifting toward whoever has the most persuasive demo.

Step 3: What data signals do you need — and does the vendor have them natively?

• List all data signals currently available in your stack: device, identity, behavioral, transactional, post-fulfillment
• Identify gaps — signals you need but don’t currently capture
• Verify that shortlisted platforms can ingest, enrich, and act on your required datasets
• Ask vendors which signals they hold natively versus access through third-party integrations

Step 4: How accurate and fast is the platform on your actual transaction data?

• Run historical transaction data through vendor models — use the same dataset for all vendors
• Request detection rate and false positive rate at multiple threshold settings, not just the vendor’s preferred configuration
• Require p50, p95, and p99 latency data at peak transaction load
• For vertical-specific merchants: ask vendors to identify which vertical-native signals contributed to each POC decision
• If evaluating an FLS vendor: ask them to score your historical declines and quantify the low-risk population within that declined volume

Step 5: Will the platform fit how your fraud operations team actually works?

• Demo the case management interface with your fraud operations team — they are the daily users
• Confirm case routing, escalation rules, and SLA management capabilities
• Evaluate chargeback and dispute workflow integration
• Assess analyst tooling: can reviewers access full customer history within the platform, or do they need to switch systems?

Step 6: Can the vendor document compliance with GDPR, PSD2, and PCI DSS?

• Request compliance documentation for each applicable framework (GDPR, PSD2/SCA, PCI DSS)
• Confirm data retention and deletion capabilities
• Verify audit trail completeness for regulatory review purposes

Step 7: How do you pilot the platform and optimize it over time?

• Define baseline metrics before pilot launch — do not rely on vendor-reported baselines
• Run A/B testing where possible: compare false positive rates and authorization rates against current solution
• Establish a cadence for model review and threshold adjustment during the pilot period
• Document the optimization process: what changed, why, and what the result was

How Should a Buying Team Run a Fraud Prevention Platform Evaluation?

Most buying guides for fraud prevention platforms focus on what to evaluate. Far fewer address how to run the evaluation process — particularly in organizations where this decision touches multiple teams with competing priorities. Fraud platform selection is rarely a single-stakeholder decision, and treating it as one is a common reason implementations stall or underperform post-launch.

Who should be involved in evaluating a fraud prevention platform?

What is the right process for running a fraud platform evaluation?

1. Align on decision criteria before vendor demos. Have each stakeholder group identify their top three must-haves and one deal-breaker. Resolve conflicts at the criteria level — not after demos, when vendor preference has already formed.
2. Assign stakeholder leads to specific evaluation tracks. Fraud ops evaluates detection accuracy, vertical model depth, and workflow. Engineering evaluates integration. Finance evaluates pricing and ROI. CX evaluates approval rate and friction impact. Each group scores independently before the team convenes.
3. Run a structured proof-of-concept with shared data. Request that vendors run historical transaction data through their models and report detection rates, false positive rates, and latency. Use the same dataset across all vendors. For merchants in specialized verticals, explicitly ask vendors to demonstrate which vertical-specific signals contributed to each decision during the POC — a vendor whose model performs well on aggregate metrics but cannot explain vertical-native signal contribution may be running a generic model that will underperform on your highest-risk transaction types.
4. Hold a single cross-functional scoring session. After independent evaluation, convene to share scores and surface disagreements. Trade-offs should be explicit decisions, not hidden compromises.
5. Document the decision rationale. Record not just which vendor was selected but why, and what was deliberately traded off. This protects the decision during implementation and provides a baseline for post-launch performance reviews.

How do you resolve disagreements between fraud, CX, finance, and engineering during platform selection?

Fraud accuracy vs. customer experience. Fraud ops will generally prefer stricter models; CX will advocate for looser thresholds to protect approval rates. Resolve by agreeing on a false positive rate ceiling before selection, not after.

Feature richness vs. implementation speed. Calculate the cost of fraud losses during a delayed deployment against the long-term performance differential. The answer is usually not as close as it feels during the selection process.

Known vendor vs. best-fit platform. Brand recognition is not a proxy for vertical performance. Weight demonstrated results in your specific industry over logo recognition.

What Integration and Scalability Factors Should Retailers Evaluate?

A fraud prevention platform is only as effective as its integration into your existing stack. The following considerations are frequently underweighted during selection and overweighted during implementation regret.

• API-first architecture. Confirm the platform offers a well-documented, versioned API that does not require proprietary SDKs for core functionality.
• Ecommerce platform compatibility. For Shopify and Salesforce Commerce Cloud merchants, confirm native integrations are maintained and current. Learn more about Shopify-specific fraud prevention considerations.
• Horizontal scalability. Confirm the platform has demonstrated performance at transaction volumes above your current peak — not just your current average.
• Integration timeline. Ask for median implementation timelines for merchants of your size and stack complexity — not the fastest implementation on record.
• Coverage during transition. Understand what happens to fraud coverage during the integration window if you are migrating from an existing solution.

How Do You Balance Fraud Prevention with Customer Experience and Approval Rates?

The false positive problem is not a calibration issue to be managed after platform selection — it is a primary evaluation criterion that should drive platform choice. Merchants who treat approval rate as a secondary metric to fraud rate will consistently undercount the revenue cost of their fraud prevention decisions.

The most effective platforms achieve strong protection without sacrificing legitimate traffic through:

• Adaptive 3DS authentication — challenging only the transactions that warrant it, rather than applying friction uniformly
• Continuous model tuning — thresholds that can be adjusted in near-real-time without engineering involvement
• Longitudinal customer recognition — using established customer history to fast-track known good buyers through lower-friction paths
• Segment-specific rules — different risk thresholds for different customer segments, channels, and transaction types

For more on protecting legitimate revenue while managing first-party fraud and returns abuse, see Accertify’s dedicated resource on the topic.

What Are the Most Important Fraud Prevention Trends to Watch in 2026 and Beyond?

The following trends are shaping platform development and merchant strategy in 2026 and beyond:

Agentic commerce as a new fraud surface. As AI-powered shopping agents begin executing purchases autonomously on behalf of consumers, the question merchants must ask shifts from “is this a bot?” to “is this a bot I can trust?” Per Javelin Strategy & Research’s 2026 Fraud Management Trends report, agentic commerce is emerging as a distinct fraud vector requiring new signal categories and decisioning logic that most current platforms were not designed to address.

Behavioral biometrics maturation. Device fingerprinting and IP signals are increasingly commoditized. The next generation of identity signals — keystroke dynamics, touch pressure, scroll velocity, and mouse movement patterns — offer harder-to-spoof behavioral identifiers that distinguish humans from automation and legitimate users from account takeover actors.

Vector databases and pattern recognition at scale. Specialized data stores that hold complex pattern embeddings are enabling faster recognition of novel fraud tactics that do not match historical fraud signatures — a critical capability as AI-generated attacks produce fraud patterns that have no prior training data equivalent.

Privacy-first analytics. As GDPR enforcement intensifies and similar frameworks expand globally, platforms are under increasing pressure to deliver high-accuracy fraud detection with minimal personal data collection. Privacy-preserving machine learning techniques and on-device signal processing are becoming competitive differentiators, not compliance accommodations.

Cross-merchant intelligence sharing. Fraud rings attack multiple merchants simultaneously. Platforms with consortium data — anonymized shared intelligence across their merchant base — can identify emerging attack patterns earlier than single-merchant data allows. This is one of the strongest arguments for scale in fraud prevention: more transactions means faster pattern recognition.

How Do You Avoid Falling for Vendor Hype When Evaluating Fraud Prevention Platforms?

Fraud prevention is a category with a high density of compelling claims and a low density of independently verifiable proof. Every vendor offers “AI-powered detection,” “real-time decisioning,” and “industry-leading accuracy.” Almost none of those claims come with the methodology, the dataset, or the baseline comparison needed to evaluate them. The following framework gives buying teams the tools to separate signal from noise.

How do you evaluate a vendor’s fraud detection performance claims?

Any vendor can say they “reduce fraud by up to 80%.” The words “up to” are doing all the work. For every performance claim, ask four questions: What was the baseline? What dataset was used? What time period? And what was the false positive rate at that performance level?

Detection rate without false positive rate is meaningless — any model can catch 100% of fraud if it declines everything. A vendor unwilling or unable to answer these four questions is not withholding proprietary information. They are telling you the claim has no foundation.

Should you trust vendor case studies and reference customers?

Case studies are curated. Reference customers are selected. A vendor’s best performance numbers almost always come from their best customers, under their best conditions, in their best vertical. The only number that matters for your business is how their model performs on your transaction data — your fraud patterns, your customer base, your product mix.

Insist on a proof-of-concept using your own historical data before any commitment. If a vendor declines to run a POC, that is the most important data point they have given you.

What is the difference between model transparency and genuine model control?

Many vendors offer “explainability” dashboards and reason codes for their decisions. These are valuable — but they can also be cosmetic. Ask vendors to demonstrate not just that they can show a reason code, but that your fraud operations team can act on it: Can you modify the weight of a specific signal? Can you suppress a signal that is causing false positives in your market? Can you add a custom signal from your own data?

A model that shows you what it decided but does not let you influence how it decides is a black box with a window. Transparency that does not translate to control is presentation, not partnership.

How do you verify that a vendor’s latency claims hold up under real transaction volumes?

Sub-100ms decisioning is a standard claim. It is also frequently measured under ideal lab conditions — low transaction volume, clean data payloads, no integration latency. Ask vendors to provide p50, p95, and p99 latency at peak transaction load. P99 latency is the number that affects real customer checkout experiences during your highest-traffic periods — which are also your highest-fraud-risk periods.

How realistic are vendor claims about easy integration and fast implementation?

“Easy integration” and “plug-and-play” are among the most reliably optimistic claims in enterprise software. Ask vendors for the median implementation timeline for a merchant of your size and stack complexity — not their fastest on record. Ask what happens to your fraud coverage during the integration window.

What does “AI-powered fraud prevention” actually mean — and how do you tell the difference between real ML and a relabeled rules engine?

The term covers an enormous range of actual capability — from basic logistic regression models labeled as “machine learning” to genuine multi-model orchestration architectures that layer rules engines, supervised ML, graph networks, and sequential transformer models simultaneously. Ask vendors to describe specifically: what model architecture underlies their risk scoring, how frequently the model retrains, on what data, and who controls the retraining cycle. Ask whether their “AI” is the primary decisioning layer or a feature that augments a rules-based system.

There is also a subtler question worth asking: does the vendor replace older models with newer ones, or orchestrate them together? A vendor who leads with their newest transformer model as proof of sophistication — while deprecating their rules engine and ML models — is making a category error. Each model generation was built to catch a different type of fraud. Velocity rules catch simple high-speed attacks that a transformer model may never flag. Graph networks detect fraud ring relationships that supervised ML models miss entirely. Sequential transformers understand behavioral context that rules engines cannot represent. Removing any layer removes a detection capability.

The most sophisticated fraud prevention platforms are not the ones with the newest single model. They are the ones that orchestrate multiple model generations together, routing each transaction through the models best suited to its fraud risk profile. Ask vendors not just “what is your newest model?” but “how do your models work together, and what does each one catch that the others don’t?”

What single question best reveals whether a fraud vendor is operating in good faith?

After every vendor presentation, ask one question: “What would you tell us not to expect from your platform?”

A vendor who answers that question honestly — naming real limitations, real integration complexity, real edge cases where their model underperforms — is a vendor operating in good faith. A vendor who cannot identify a single limitation of their own platform is either unaware of them or unwilling to share them. Neither is a foundation for a long-term risk management partnership.

Ecommerce Fraud Prevention: Frequently Asked Questions

How do I implement AI fraud detection for my ecommerce site?

Start by mapping your specific fraud exposure — which fraud types affect your business most, at what volume, and at which points in the customer journey. Then evaluate AI-powered platforms that offer adaptive machine learning, device fingerprinting, and behavioral analytics native to your vertical. Prioritize platforms that can demonstrate performance on your transaction data before implementation, and confirm their integration timeline against your technical stack. For Shopify merchants, see Accertify’s Shopify-specific guidance.

What key features should I look for in a fraud prevention platform?

Prioritize seven capabilities: breadth of native signal sources, adaptive machine learning with configurable business rules, real-time risk scoring at sub-100ms latency, lifecycle signal coherence across the full customer journey, vertical-specific models calibrated to your industry, integrated case management tools, and documented compliance support for applicable regulations. Do not evaluate features in isolation — the most important question is how these capabilities work together to maximize accurate approvals, not just minimize fraud losses.

What is the real cost of a fraud liability shift model?

Fraud liability shift (FLS) arrangements transfer financial responsibility for approved fraudulent transactions from the merchant to the vendor. The appeal is real — the merchant is protected from fraud losses on approved orders. But the structure creates a misalignment with significant revenue consequences. When a vendor holds fraud liability, their model is optimized to protect their own financial exposure, not to maximize the merchant’s approvals. Their incentive at the margin is always to decline: a declined transaction costs them nothing; an approved transaction that turns fraudulent costs them directly.

The result is systematic over-declining at the edges of the risk distribution — which is precisely where recoverable revenue concentrates. In a documented analysis of a single merchant’s high-risk instant ticketing transactions over one quarter, a liability shift provider declined approximately $4.9 million in transactions. Of that declined volume, $1.087 million fell into the low-risk scoring range of a validated vertical travel model. The estimated fraud exposure on that $1.087 million was $2,091. Net recoverable revenue: $1.085 million.

The merchant left over a million dollars in safer sales on the table — not because the risk was unacceptable, but because the vendor’s model was never designed to make that calculation on the merchant’s behalf. Before committing to any FLS arrangement, ask the vendor to score your historical declined transactions and show you the low-risk population within that declined volume. A vendor confident in their model’s accuracy will welcome that analysis.

How do pricing models typically work for ecommerce fraud prevention platforms?

Most platforms offer tiered SaaS pricing structured as either a fixed monthly fee, a percentage of transaction value processed, or a per-decision fee. Some vendors offer guarantee or advisory models where they accept financial liability for approved transactions. When comparing pricing, evaluate total cost of ownership including implementation fees, integration costs, and ongoing configuration and support — not just the per-transaction or monthly rate. For FLS models specifically, factor in the revenue cost of declined transactions that a more accurate model would have approved.

What fraud trends should ecommerce merchants watch in 2026?

The highest-priority emerging threats are: AI-assisted account takeover and synthetic identity fraud operating at machine speed; agentic commerce fraud as AI shopping agents become a new attack surface; and increasingly coordinated fraud rings that attack multiple merchants simultaneously, making cross-merchant intelligence sharing a competitive advantage for platforms with large consortium datasets. On the regulatory side, SCA enforcement in Europe and expanding data privacy frameworks globally are adding compliance requirements that make platform architecture choices today consequential for the next several years.

How can fraud prevention support compliance with regulations like SCA and GDPR?

Fraud prevention platforms support compliance through several mechanisms: native 3DS2 implementation for SCA compliance, behavioral analytics that enable step-up authentication only when risk warrants it (reducing friction while meeting regulatory requirements), data minimization architectures that limit personal data retention, and audit trail capabilities that document decisioning for regulatory review. Verify that vendors can provide specific documentation for each applicable framework — not generic compliance language — and confirm that their data handling practices are compatible with your organization’s privacy obligations.

Related Resources

• The Accertify Fraud Detection Platform
• White Paper: The Evolution of Fraud Detection Models — From Velocity Checks to Sequential Transformer Models
• Accertify for Retail
• Shopify Fraud Prevention Guide
• Salesforce Fraud Detection and Prevention
• First-Party Ecommerce Fraud Prevention: 8 Tips
• Case Study: ASDA Selects Accertify for Ecommerce Fraud Prevention

References

1. LexisNexis Risk Solutions. True Cost of Fraud Study: Ecommerce and Retail Report — US and Canada Edition (2025). risk.lexisnexis.com
2. MRC / Visa Acceptance Solutions / Verifi / B2B International. 2025 Global eCommerce Payments & Fraud Report. cybersource.com
3. MRC / Visa Acceptance Solutions. 2026 Global eCommerce Payments & Fraud Report. merchantriskcouncil.org
4. Javelin Strategy & Research. 2026 Fraud Management Trends. javelinstrategy.com
5. Javelin Strategy & Research. 2025 Identity Fraud Study. javelinstrategy.com