Fraud Prevention Platforms Compared: Guarantee, Managed Decisioning, and Risk Scoring Models

Fraud prevention platform selection is routinely framed as a vendor question. It is not. The decision that matters is which operating model fits your fraud maturity, team structure, margin tolerance, and chargeback exposure — and vendor selection follows from that. Getting the model wrong is more expensive than getting the vendor wrong.

Three models account for the dominant platforms in the market. Guarantee/liability-shift vendors approve or decline orders and assume chargeback liability on approved transactions for a fee. Managed-decisioning vendors automate the approve/decline call and offer contractual commitments on approval and chargeback outcomes, without purely insuring the outcome. Risk-scoring/decisioning vendors return a score or recommendation that the merchant's team acts on, keeping the decision and the chargeback liability in-house.

Fraud tools vs chargeback tools

Fraud prevention platforms and chargeback management platforms get evaluated as if they compete for the same budget line. They don't. They sit at different points in the same dispute lifecycle, and a mature fraud operation typically runs one of each rather than picking a single winner across categories.

Pre-auth fraud scoring is what this article compares: Signifyd, Riskified, Forter, Kount, and Sift all operate before authorization, either deciding to approve or decline the transaction directly or returning a score your team acts on — depending on the operating model above. This layer determines how much fraud reaches your books in the first place.

Pre-dispute deflection sits one stage later — after a transaction is authorized but before a chargeback files. Verifi (Visa) and Ethoca Alerts (Mastercard) are network-side programs, not merchant SaaS: they let an issuer's dispute inquiry get resolved — a refund, a credit, a data exchange — before it converts into a formal chargeback.

Post-chargeback representment is the final stage. Chargeflow and Justt (merchant-configured automation) and Chargebacks911 (managed service) contest chargebacks that have already been filed.

Comparing a fraud-scoring vendor against a representment vendor is a category error — they solve different problems on opposite sides of the authorization line. For the deflection and representment layers in depth, see how to compare chargeback management platforms.

The short answer

If your fraud team is lean or non-existent, and predictable fraud cost matters more than optimising to the lowest possible fraud rate, a guarantee or managed-decisioning model reduces operational lift and makes fraud cost a line item rather than a variable. If you have a mature risk function with analysts, rules infrastructure, and the appetite to own decisions, a risk-scoring model gives you control and customisation at lower total cost — assuming your fraud rates support it.

The comparison below covers six vendors that publicly position across these models. Claims are sourced from vendor product pages accessed 2026-05-30, 2026-07-07, or 2026-08-20 (see each fact's dateAccessed). Verify each vendor's current positioning, coverage scope, pricing structure, and exclusions directly — these change, and published marketing metrics are vendor-reported, not independent benchmarks.

The three models explained

Guarantee / liability-shift

The vendor approves or declines each transaction. On orders the vendor approves, it assumes financial liability for fraud chargebacks. The merchant pays a fee on approved orders and receives reimbursement when an approved order generates a qualifying chargeback.

What this changes operationally: fraud cost becomes a predictable fee rather than a variable loss rate. The vendor is incentivised to approve legitimate orders accurately — they bear the loss if they approve fraud. Dispute operations (representment, evidence collection) may be handled by the vendor, reducing in-house chargeback team requirements.

What this does not change: non-fraud losses (operational errors, policy abuse, refund abuse, first-party fraud) are not automatically covered — coverage scope and exclusions are commercial terms, not published on product pages. Verify these directly.

Vendors publicly positioning in this model (as of 2026-05-30, ClearSale added 2026-08-20): Riskified, Signifyd, ClearSale (ClearSale's guarantee applies only to its higher-tier decision plans — verify tier eligibility).

Managed decisioning

The vendor automates the approve/decline decision using AI, machine learning, and identity-graph data. Rather than pure liability insurance, vendor materials describe contractual commitments covering approval rates, chargeback rates, and response time SLAs — a performance-based model rather than a per-order guarantee.

What this changes operationally: the vendor makes real-time decisions without requiring the merchant to act on a score. The contractual commitment is on outcomes (approval and chargeback rates), not on per-order reimbursement in the same structure as a guarantee model. Dispute management may be available as a separate product.

What this does not change: the merchant still needs to configure the integration, handle edge cases, and maintain a working relationship with the vendor's dispute/chargeback team. Liability mechanics differ from a pure guarantee — verify the exact contractual structure.

Vendors publicly positioning in this model (as of 2026-05-30): Forter.

Risk scoring / decisioning

The vendor returns a fraud score, risk indicators, and/or a recommendation. The merchant's own team — or the merchant's internal rules engine — makes the final approve/decline decision and retains chargeback liability. The vendor provides the intelligence layer; the merchant owns the decision and its consequences.

What this changes operationally: control and tuning sit with the operator. A risk team can override scores, segment decisioning by product or channel, and build custom rules on top of the vendor score. Case management tooling helps analysts review flagged transactions.

What this does not change: the merchant requires internal capacity to act on signals, set thresholds, investigate false positives, and tune decisioning logic. There is no liability transfer; fraud losses remain the merchant's responsibility.

Vendors publicly positioning in this model (as of 2026-05-30): Sift, Kount (Equifax).

Vendor comparison

Important sourcing note. Claims in this table are drawn from vendor product pages accessed 2026-05-30, with Forter's, Sift's, and Signifyd's network-scale figures and ClearSale's row refreshed 2026-08-20 (see sources[] for exact dates). Vendor positioning, products, and commercial terms change. Re-verify at evaluation time. Marketing performance metrics (detection rates, chargeback-reduction percentages, approval-uplift figures) are excluded from this table — these are vendor-reported, vary by customer base, and are not independent benchmarks. (Approval/false-positive figures vendors do publish are covered, and explicitly attributed as vendor claims, in the head-to-head pairs below.)

Evaluating vendors head-to-head

The query that brings most readers to this page is a "vendor A vs vendor B" search — Forter vs Riskified, Signifyd vs Riskified, Sift vs Riskified, Kount vs Signifyd, and the same pairs in reverse order. None of these pairs has a publicly correct answer, because none of the underlying performance numbers are public. What follows is what to evaluate, not who wins.

Dimensions that matter for any pair:

• Decision latency and where the decision sits. Some vendors decide inline at checkout; others return a score your team acts on afterward. This is the operating-model axis covered above, and for a head-to-head it is the first question — it determines whether you are comparing two decision engines or a decision engine against a scoring tool.
• Guarantee vs decisioning-only posture. Does the vendor assume chargeback liability on approved orders, commit contractually to approval/chargeback outcomes, or return a score and leave liability with you? Similar guarantee language can still hide different exclusions and reason-code scope — ask for the full exclusion schedule, not the marketing page.
• Pricing model shape. Fee on approved GMV, per-transaction scoring fee, or a guarantee premium — the shape matters more than any published rate, since none of the five vendors here disclose rates. Evaluate fit against your margin structure, not a number you cannot get anyway.
• Integration surface. Platform/PSP plugin depth versus API-first integration changes time-to-live and maintenance burden. Ask each vendor for their current integration list against your stack — public pages understate this.
• False-positive handling and appeal loops. Ties to the RFP checklist below — a vendor that wins on headline catch rate but has a slow, opaque override process can cost more in blocked good customers than it saves in fraud.
• Data-network scale. Vendors publish network size (identities, sites, signals) as a quality proxy. Treat it as a question to verify at evaluation time, not a comparable metric — vendors define "identity" and "signal" differently, and the figures are vendor-reported.

On the specific pairs: Forter vs Riskified is a managed-decisioning-vs-guarantee comparison, not two versions of the same product — evaluate posture fit, not which vendor "performs better" in the abstract. Signifyd vs Riskified are both guarantee-model vendors; the real evaluation is coverage scope and reason-code exclusions, which sit in the RFP rather than the public page. Sift vs Riskified and Kount vs Signifyd are both risk-scoring-vs-guarantee comparisons — the "vs" query itself usually reflects a model question (own the decision, or transfer it) rather than a feature comparison between comparable products. Riskified vs Forter and Riskified vs Signifyd are the same comparisons in reverse order; query direction doesn't change the evaluation.

None of these pairs should resolve to a declared winner. The honest method is a parallel bake-off on your own traffic, per the methodology below, run against two or more vendors in shadow mode at once — vendor-published performance claims aren't comparable to each other because they're measured against different traffic bases and different definitions of a "catch." Your own numbers, on your own traffic, are the only numbers that resolve a head-to-head.

Five pairs, compared directly

The section above sets out what to evaluate. This section applies it to the five pairings that generate the most direct comparison searches — Forter vs Signifyd, Sift vs Riskified, Forter vs Riskified, Sift vs Signifyd, and Kount vs Riskified — on the specific dimensions those searches ask about: guarantee coverage, approval/false-positive posture, decisioning model, pricing shape, dashboards, and best-fit use case. As above, none of this resolves to a declared winner — vendor-reported figures are marked as such throughout.

Forter vs Signifyd

Guarantee coverage. Signifyd runs a guarantee/liability-shift model: it states its Complete Chargeback Protection covers fraud and non-fraud chargebacks, including INR (item not received) and SNAD (significantly not as described), on approved orders. Forter runs managed decisioning instead of a per-order guarantee — its public materials describe contractual commitments on approval rates, chargeback rates, and response-time SLAs, and Forter itself explicitly contrasts this structure with insurance-style guarantees. If you need INR/SNAD specifically covered by a financial guarantee, Signifyd's public positioning is the closer match; if you want a vendor-run decision engine with outcome-based commitments rather than per-order reimbursement, Forter's is.

Approval-rate and false-positive posture. Signifyd states a "5-9% increase in approvals" for merchants on its guarantee. Forter's public page carries a customer quote stating "our approval rating has gone up," with no percentage attached. Both are vendor claims — Signifyd's is a quantified vendor figure, Forter's is an unquantified customer testimonial; neither is independently audited.

Decisioning model. Forter positions as real-time automated AI decisioning across identity, behavioral, device, and payment signals, protecting "every payment channel" it lists — cards, wallets, BNPL, ACH, and gift cards — and offers an "Analytics Agent" that assembles transaction context for analyst investigation. Signifyd positions as automated risk decisioning that issues a fraud score on all traffic in addition to the approve/decline call.

Pricing model. Neither vendor publicly discloses rates. Forter's structure is a performance-based commitment (not a per-order insurance premium in the same structure as a guarantee fee); Signifyd's is a guarantee fee on approved orders. Evaluate the shape against your margin structure — you will not get a comparable published number from either.

Dashboards and reporting. Forter's Analytics Agent is the one dashboard-adjacent capability either vendor discloses publicly; Signifyd's reporting and dashboard tooling was not detailed on the pages reviewed — ask directly.

Best fit. Forter fits merchants wanting one vendor to run decisioning across a wide payment-method mix (including BNPL and ACH) under outcome-based contractual commitments. Signifyd fits merchants who specifically need non-fraud reason codes (INR, SNAD) financially guaranteed, and who value Signifyd's stated enterprise merchant network — verify current network scale and contract terms directly, since this is vendor-stated positioning.

Sift vs Riskified

Guarantee coverage. These two sit on opposite ends of the liability spectrum. Sift is risk-scoring only — Sift returns a score and recommendation; the merchant's team makes the call and keeps the liability. Riskified is a guarantee model — it assumes chargeback liability on orders it individually reviews and approves, publicly stating "we guarantee approval rates and cover any chargebacks." If you want liability off your books, Sift does not offer that; Riskified's public model does, on the orders it approves.

Approval-rate and false-positive posture. Sift states a 99.4% acceptance rate, "fewer false positives without sacrificing protection," and 70-72% fewer manual reviews across its public materials and case studies, including one case study citing 6x ROI. These are vendor-reported and case-study-derived figures, not independently audited or necessarily representative of your traffic. Riskified's public guarantee language commits to approval rates and chargeback coverage on approved orders, but a specific approval-rate percentage was not confirmed via direct source access for this update — do not assume a figure that is not on the vendor's current page; ask for it directly in the RFP.

Decisioning model. Sift combines AI/ML-driven real-time scoring with a merchant-configurable "Decisioning Engine" for building and adapting rules — control sits with your team on top of Sift's models. Riskified's vendor-run engine makes the approve/decline call per order, with Dispute Resolve handling representment on approved-but-disputed orders and Policy Protect as a separate product for policy abuse and friendly fraud.

Pricing model. Neither publishes rates. Sift's shape is a scoring/platform fee (the merchant retains liability, so there is no guarantee premium); Riskified's shape is a fee on approved orders, consistent with its liability-shift structure.

Dashboards and reporting. Sift publicly names a "Sift Console" for investigation and manual review, plus reporting, case management, and workflow-analysis tooling. Riskified's Dispute Resolve is positioned as handling representment on the merchant's behalf; further dashboard and reporting specifics were not independently confirmed via direct source access this pass — verify directly rather than assuming parity with Sift's disclosed tooling.

Best fit. Sift explicitly names "Commerce Marketplaces" — buyers, sellers, and transactions — as a distinct use case, alongside B2B/B2C software, iGaming, travel, food delivery, and fintech, which makes it a natural fit for merchants with in-house risk capacity operating a marketplace model. Riskified fits merchants — per its existing public positioning, e-commerce with Shopify and enterprise integrations — who want liability transferred and dispute representment handled by the vendor rather than owning it internally.

Forter vs Riskified

Guarantee coverage. Riskified is a guarantee model on individually approved orders; Forter is managed decisioning with contractual approval/chargeback-rate and SLA commitments rather than a per-order insurance structure. This is a posture question before it is a performance question — decide whether you want per-order liability transfer (Riskified's public model) or a vendor-run decision engine with outcome commitments (Forter's).

Approval-rate and false-positive posture. Forter's page carries an unquantified customer testimonial that approval rating "has gone up." Riskified's public guarantee language commits to approval rates and chargeback coverage but a specific current percentage was not confirmed via direct source access for this update. Treat both as vendor-side claims to verify, not comparable numbers.

Decisioning model. Forter's decisioning spans "every payment channel" it lists, including BNPL, ACH, and gift cards in addition to cards and wallets, using automated AI across identity, behavioral, device, and payment signals in real time. Riskified decides per order and separates representment (Dispute Resolve) and policy-abuse/friendly-fraud handling (Policy Protect) into distinct products alongside the core guarantee.

Pricing model. Neither discloses rates. Forter's is a performance-based commitment structure; Riskified's is a fee on approved orders — the guarantee-fee shape typical of liability-shift vendors.

Dashboards and reporting. Forter's Analytics Agent assembles transaction context for analyst investigation. Riskified's Dispute Resolve is positioned as representment handling rather than a general merchant dashboard; broader reporting specifics were not independently confirmed this pass — verify directly.

Best fit. Forter fits merchants who want multi-payment-channel breadth (BNPL, ACH, wallets, gift cards) under one automated decision engine without a per-order insurance framing. Riskified fits merchants who want explicit per-order liability transfer plus a dedicated representment product and separate abuse-specific coverage — typically e-commerce operators, per its stated Shopify and enterprise integration focus.

Sift vs Signifyd

Guarantee coverage. Sift is risk-scoring only — no liability assumption, merchant retains the decision and the chargeback exposure. Signifyd is a guarantee model, with its public materials specifically naming fraud and non-fraud chargebacks (INR, SNAD) as covered on approved orders. If non-fraud reason codes need financial coverage, that is Signifyd's stated territory, not Sift's.

Approval-rate and false-positive posture. Sift states a 99.4% acceptance rate and 70-72% fewer manual reviews across its public materials. Signifyd states a "5-9% increase in approvals," and separately frames the industry problem it addresses — that fraud accounts for roughly 1% of transactions yet over 10% of orders are routinely rejected defending against it. Both figures are vendor-stated; neither is independently audited.

Decisioning model. Sift pairs AI/ML scoring with a merchant-configurable Decisioning Engine, keeping rule-building control with your team. Signifyd automates the approve/decline call itself and additionally issues a fraud score across all traffic — control sits with Signifyd on guaranteed orders, with the score available as a secondary signal.

Pricing model. Neither vendor publicly discloses rates. Sift's shape is a scoring fee (no guarantee premium, since liability stays with the merchant); Signifyd's shape is a guarantee fee on approved orders.

Dashboards and reporting. Sift's Console, plus its case-management and workflow-analysis tooling, is the more publicly documented of the two. Signifyd's dashboard and reporting capabilities were not detailed on the pages reviewed — ask directly.

Best fit. Sift's explicit "Commerce Marketplaces" segment (buyers, sellers, and transactions) makes it a natural evaluation candidate for marketplace merchants with in-house risk capacity. Signifyd fits merchants who want non-fraud reason codes guaranteed and who value its stated position as the "largest enterprise merchant network on the market" — verify current scope and terms directly.

Kount vs Riskified

Guarantee coverage. This is the most structurally different pair on this page, not a close call between similar products. Kount (Equifax) is risk-scoring / identity-trust: Kount 360 is publicly positioned around AI- and machine-learning-driven scoring, with no chargeback guarantee stated in Equifax's own materials — the merchant's team or rules engine makes the final approve/decline call and keeps the chargeback liability. Riskified is a guarantee model: it takes on liability for orders it individually reviews and approves, publicly stating "we guarantee approval rates and cover any chargebacks." A search for "Kount vs Riskified" is usually really asking whether to keep the decision (and the liability) in-house or transfer it — not which vendor scores transactions more accurately.

Approval-rate and false-positive posture. Riskified's public materials describe an approval-rate service-level commitment sitting alongside the chargeback guarantee on approved orders, but a specific current percentage was not confirmed via direct source access for this update — treat any rate quoted to you in an RFP as deal-specific, not a published figure. Kount does not publish an approval-rate or false-positive figure on the pages reviewed for this article. Neither number is comparable as published; request each vendor's methodology and a pilot run on your own traffic.

Decisioning model. Kount 360 runs supervised and unsupervised machine-learning models to contextualize transaction data and produce a risk score, with capabilities Equifax's own announcement describes as "automated or adjusted to fit the needs of a business" — the merchant chooses how much of that automation to trust versus routing cases to analyst review. Riskified decides per order directly, with a dedicated Dispute Resolve product handling representment on approved-but-disputed orders and Policy Protect sold separately for policy abuse and friendly fraud.

Pricing model. Neither vendor publicly discloses rates, and neither's reviewed materials specify the pricing structure in detail. Kount's shape — scoring fee, subscription, or platform fee — is not stated on the Equifax pages reviewed for this article; ask directly rather than assuming a shape. Riskified's shape is a fee charged per approved order, consistent with its liability-shift structure and its stated incentive to approve accurately rather than over-decline, since it bears the cost of an approval that turns into fraud.

Dashboards and reporting. Equifax's own Kount 360 materials describe "an intuitive single user interface" built for analysts to build and deploy rules quickly; deeper case-management specifics were not independently confirmed this pass. Riskified's Dispute Resolve is positioned as handling representment on the merchant's behalf; broader dashboard and reporting detail was likewise not independently confirmed via direct source access this pass — ask both vendors directly.

Best fit. Kount fits merchants who want to keep the decision and its liability in-house — typically because they already have, or are building, risk-team capacity to act on scores — and for whom the Equifax identity-data lineage is a plus rather than a data-sharing question to resolve first. Riskified fits merchants who want per-order liability transferred and representment handled by the vendor, typically e-commerce operators without the internal capacity or appetite to own chargeback outcomes directly. This pairing rarely resolves on features; it resolves on whether your organisation wants to own the decision at all.

What public pages do not tell you — and what to ask in the RFP

The comparison table captures publicly verifiable positioning. The following dimensions determine day-to-day operational reality and financial exposure — and none are available from product pages. Take these as your RFP checklist.

Liability scope and exclusions What categories of chargeback are excluded from the guarantee or contractual commitment? Common exclusion areas: orders the vendor declined but the merchant approved via override; orders placed using the merchant's own gift cards or credits; transactions in specific geographies; orders that failed to meet data submission requirements. Ask for the full exclusion schedule in writing.

Covered vs non-covered chargeback reason codes Signifyd's public pages explicitly mention INR and SNAD coverage. Riskified's pages reference "any chargebacks" with specific exclusions in their support documentation. Neither publishes a full reason-code coverage matrix. For risk-scoring models (Sift, Kount), the merchant bears all liability regardless of reason code — the question shifts to how the score integrates with your dispute operations. Ask each guarantee/decisioning vendor for a complete list of covered Visa and Mastercard reason codes.

First-party fraud and friendly fraud First-party fraud — a customer making a legitimate purchase and then disputing it — is typically the hardest category to cover under a standard fraud guarantee, because it generates a chargeback with a dispute reason code that looks like the customer never authorised the transaction. Riskified's Policy Protect is a separate product for this category. Ask vendors explicitly whether first-party fraud chargebacks are covered, and under what evidentiary standard they will contest a dispute filed by a returning customer.

INR, service disputes, refund abuse, and policy abuse Item Not Received (INR) and Significantly Not As Described (SNAD) generate chargebacks under non-fraud reason codes and require different evidence to contest. Refund abuse (requesting a refund after consuming goods or services) and policy abuse (exploiting return/cancellation policies) may not generate chargebacks at all — they affect refund rates and margin, not chargeback ratios. Verify which products within each vendor's portfolio cover which abuse type, and what the operational handoff looks like.

Merchant override rights Can you approve a transaction the vendor has declined? For guarantee models, overriding a vendor decline typically removes the guarantee on that order — the liability remains with the merchant. The commercial terms around overrides (how many, which categories, what documentation) are not on product pages. For managed-decisioning models, the override architecture is similarly RFP-level.

Who owns representment and evidence submission Riskified's Dispute Resolve explicitly positions as handling representment on the merchant's behalf. Forter has a Dispute Management product. For risk-scoring models, representment remains the merchant's responsibility — assess your internal chargeback team capacity against the expected dispute volume your platform choice creates. Ask each vendor what their representment win rate is for their managed service, and how they handle disputes in markets where you do not have a local entity.

SLA and support model Response time SLAs for transaction decisions are listed by some vendors; support SLAs for operational issues, escalations, and dispute handling are not. Implementation support depth (dedicated CSM, self-serve documentation, managed onboarding) varies and directly affects time-to-live and post-launch fraud coverage.

Regional and data privacy considerations GDPR, CCPA, and local data residency requirements affect what data can be shared with a US-headquartered vendor, how model training data is used, and whether EU-specific product versions are available. Kount's Equifax lineage adds credit-bureau data questions in some jurisdictions. Verify the vendor's data processing agreements, sub-processor lists, and regional product coverage before finalising contracts for non-US markets.

Feedback loop from chargebacks, refunds, and disputes How quickly does chargeback and dispute outcome data flow back into the vendor's model to improve future decisions? For guarantee models, the vendor absorbs the loss and is self-incentivised to close the loop. For risk-scoring models, closing the feedback loop requires the merchant to push dispute outcome data back to the vendor — verify the data ingestion mechanism and the lag between dispute resolution and model update. A slow feedback loop on a high-velocity fraud pattern is a direct gap.

How to run a fraud platform bake-off

Vendor case studies are marketing artefacts. A bake-off gives you your own numbers against your traffic base.

Before you start: establish baselines. Run your current fraud stack for at least 30 days before the bake-off and capture: fraud loss rate (basis points of GMV), chargeback ratio by count and by value, approval rate on attempted transactions, manual review rate, false-positive rate (sample-reviewed legitimate orders incorrectly flagged), dispute win rate by reason code, and average order value by channel. These are your comparison denominator. Without them, you are comparing against vendor marketing materials — not against your own operation.

Shadow-mode testing. Run the new platform in shadow mode: it receives your transaction data and makes decisions, but your existing system retains the live decision. Capture the shadow platform's approve/decline recommendations alongside your live decisions. Compare outcomes over a full dispute cycle (typically 90–120 days for chargebacks to resolve) before cutting over any live traffic.

Segment everything. Do not evaluate on blended metrics. Segment by: geographic region (fraud patterns differ materially by market); BIN range (issuer region affects false-positive rates); product type (digital goods vs physical goods have different fraud profiles); traffic source (direct vs affiliate vs marketplace); customer age (new vs returning — new account fraud and first-party fraud cluster differently); and transaction size band. A platform that outperforms on blended approval rate but underperforms on new-customer conversion in your highest-growth market has a negative net impact on the business you care about.

False-positive review. Assign an analyst to manually review a sample of orders the shadow platform declines — especially orders your current system approves. This is the only way to get a false-positive rate estimate that is not vendor-reported. Expect this to be the most resource-intensive part of the bake-off, and the most important for understanding the actual customer experience impact.

Manual-review workload. For risk-scoring platforms, measure the volume of transactions that land in the manual review queue (below the auto-approve threshold, above the auto-decline threshold). If the bake-off platform generates significantly more manual-review volume than your current setup, account for the analyst time this creates before calculating net benefit.

Chargeback feedback loop. Do not cut the bake-off at 30 days. Fraud detection accuracy only becomes visible when the dispute cycle closes — Visa's chargeback window is typically 120 days from the transaction date, and SEPA dispute windows differ further. Run the shadow phase long enough to see outcome data on transactions, not just decision data.

Do not rely only on vendor case studies. Vendor case studies select for successful deployments in comparable verticals. They represent the vendor's best results, not your likely results. Use them to understand the ceiling; use your own bake-off data to understand the floor.

Selection matrix by merchant archetype

What this article does not cover

Performance numbers — detection rates, approval-uplift percentages, chargeback-reduction statistics — are absent by design. All published figures in this space are vendor-reported, vary by customer base and traffic mix, and are not independently audited. Using them to compare vendors would give false precision. Your bake-off methodology produces the only numbers that are valid for your business.

Pricing is not covered because no vendor publicly discloses pricing in sufficient detail to compare. Guarantee fees as a percentage of approved GMV, scoring fees per transaction, and enterprise contract structures require direct vendor engagement.

For the mechanics of how fraud detection models work under any of these platforms, see real-time fraud decisioning, rule engines vs ML hybrid architecture, and AI fraud detection in 2026.

For how to measure your fraud operations once a platform is in place, see the fraud operations KPI scorecard.

For understanding the chargeback exposure that platform selection affects, see chargeback operations KPIs and the true cost of a chargeback.

For the dispute-side counterpart to this guide — alert networks, representment automation, and managed recovery compared by operating model — see how to compare chargeback management platforms.