Fraudio Platform comparison

SEON and Fraudio

Same fight against fraud.
Opposite ends of the payment.

SEON and Fraudio both cover fraud prevention and AML, and both use machine learning. SEON starts with the person behind the transaction. Fraudio starts with the payment flow itself. Each approach is strong in some settings and limited in others.

SEON

Fraud prevention, identity verification and AML for businesses that serve consumers directly.

Founded in BudapestHQ Austin5,000+ customersSeries C, 2025
Fraudio

Payment fraud detection, merchant risk and AML for acquirers, PSPs, processors and payment networks.

Founded in AmsterdamEurope, LATAM, SE AsiaShared network model

Vendor 1

SEON

SEON builds a picture of the user from their digital footprint, device and behaviour, then combines it with rules and machine learning scoring. The company describes its platform as primarily for B2C digital transactions.

+Strengths

  • Rich user signalsChecks email, phone and IP against more than 350 online platforms, and fingerprints devices through web and mobile SDKs.
  • Mature identity suiteDocument checks, liveness, face match and eKYC, with a long track record in onboarding and account protection.
  • Broad lifecycle coverageOnboarding, login, account takeover, bonus abuse, transactions and payouts in one platform.
  • Easy entry pointSelf-serve plans from $699 a month make it accessible for smaller teams.
  • Strong market presenceLarge customer base across iGaming, ecommerce and fintech, with fresh funding from a 2025 Series C.

−Weaknesses

  • Depends on owning the checkoutIts strongest signals come from an SDK on the page or app. Institutions that only see payment messages, such as acquirers, have little of this data.
  • No dedicated merchant risk monitoringIts payments offer focuses on end users. Merchant-initiated fraud and transaction laundering are not described as core use cases.
  • Learning is per clientEach customer's model and rules are built around its own data, so newer or smaller clients start with less history.
  • Payment-flow AML is not the centreAML screening and monitoring are available, but the platform's design centres on the customer profile rather than the payment network.

Vendor 2

Fraudio

Fraudio scores payment transactions with a single machine learning model shared across every institution on its network. Customers add their own rules on top through a no-code editor.

+Strengths

  • Built for payment dataWorks on the transaction data that acquirers and processors already hold, with responses under 100 milliseconds and no SDK on merchant sites.
  • Network-wide learningOne central model trained across all clients, so a pattern found at one institution protects the others. New customers benefit from day one.
  • Merchant risk coverageA dedicated product for merchant-initiated fraud, aimed at catching merchants whose behaviour changes after onboarding.
  • Flexible no-code rulesAn LLM-powered rule editor and a ready-made rule library that teams can deploy within minutes.
  • Low setup burdenAPI integration, pay-per-use pricing and no setup fees.

−Weaknesses

  • Narrower audienceDesigned for payment institutions. Merchants and consumer apps that want device and identity signals will find less here.
  • Younger identity offeringKYC, KYB, PEP and sanctions screening are available and tie into the same rules and case management, but this area has a shorter track record than SEON's.
  • Smaller footprintA smaller company with lower brand recognition, which some procurement teams weigh heavily.
  • Limited public benchmarksPublished performance figures come from the company itself. Buyers will usually want a backtest on their own data.

Side by side

Capability overview

AreaSEONFraudioEdge
Primary userMerchants, iGaming, marketplaces, consumer fintechAcquirers, PSPs, processors, payment networksDepends on buyer
Device and digital footprintExtensive, via SDKNot a focusSEON
Transaction scoring on payment dataAvailableCore product, shared network modelFraudio
Merchant risk monitoringNot a described use caseDedicated productFraudio
RulesCustom rules, natural-language builderNo-code LLM rule editor and rule libraryComparable
KYC, KYB, PEP and sanctionsMature, broad identity suiteAvailable, newerSEON
AML transaction monitoringAvailableBuilt on payment flows, includes money mule detectionFraudio for institutions
Time to first valueQuick self-serve start; tuning grows with dataNetwork model live from day oneDepends on volume
Pricing modelTiered plans from $699/month, custom enterprisePay-per-use, no setup feesDepends on volume

Bottom line

Which fits which buyer

SEON fits best when…

The buyer owns the relationship with the end user. Merchants, marketplaces, iGaming operators and consumer apps get the most from its device, footprint and identity signals, and from its experience in onboarding.

Fraudio fits best when…

The buyer processes payments for many merchants. Acquirers, PSPs, processors and payment networks get the most from its network-trained model, its merchant risk coverage and its focus on the data they already hold.

The two are not mutually exclusive. An institution with both an acquiring business and a consumer-facing app could reasonably run SEON on the user side and Fraudio on the payment side.

Test it against your own transactions

The fairest comparison is your own data. A backtest on your historical transactions shows what each approach would have caught.

Book a conversation