Payment fraud

Payment fraud management — controls inside, not bolted on.

topropay treats fraud controls as a first-class part of the orchestration layer. Velocity rules, list management, scheme-programme threshold tracking, partner-engine connectors, push-payment beneficiary screening and one dispute queue — all under the same unified API as authorisation and reconciliation.

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low mid high 0.62 composite risk score
  • Device fingerprint match
  • BIN / geography mismatch
  • No list match
  • New beneficiary (push rail)
Multi-rail
card · ACH · APP · crypto
Velocity
rules per merchant and segment
Lists
block / allow per vault token
1 queue
for disputes and chargebacks

Key benefits

What orchestrated payment fraud management changes

Four outcomes that show up consistently once anti-fraud controls sit inside the payment-orchestration layer rather than in a separate console or a bolted-on product.

  1. 01

    Anti-fraud controls inside the same orchestration layer

    Velocity rules, list management, scheme-programme threshold tracking and chargeback tooling sit next to the routing engine — not in a separate console. The merchant doesn't bolt on a fraud product; topropay treats fraud controls as a first-class part of the payment-orchestration surface.

  2. 02

    Bring-your-own fraud engine, or use the platform's

    Partner-agnostic connectors plug merchants' existing fraud engines (Sift, Riskified, Forter, Kount, Signifyd and similar) into the same authorisation path. Merchants that don't already run one inherit topropay's default policies tuned per vertical and chargeback band.

  3. 03

    Authorised push payment fraud — handled, not ignored

    Push-payment rails (SEPA Instant, Faster Payments, Open Banking VRP, PIX) don't carry the card-style chargeback cycle, but they do carry authorised push payment fraud risk. The platform layers beneficiary screening, mandate-review steps and (where applicable) Confirmation-of-Payee on top of the rail.

  4. 04

    One dispute and chargeback queue across providers

    Card chargebacks, ACH returns and APP-fraud disputes land in one operator queue — evidence-pack templates per vertical, automated representment for select scheme types and per-acquirer dispute timelines abstracted into one consistent surface.

Fraud taxonomy

The six categories of payment fraud the platform handles

What 'payment fraud' actually covers — split by the signal model and the rail shape. Each category gets per-merchant policies on the same orchestration surface.

  • 01

    Card payment fraud

    CNP-card fraud — stolen-card use, BIN testing, enumeration, account-takeover, refund fraud — across every connected card acquirer. Velocity rules, BIN-list management, 3DS2 step-up policies and per-merchant risk thresholds layered on top of the routing engine.

  • 02

    Credit card payment fraud (specific)

    Credit card payment fraud carries higher dispute exposure than debit because chargeback rules and refund propensity differ. The platform tracks Visa VDMP / VAMP / VFMP and Mastercard ECP / EFMP thresholds per merchant and surfaces position vs limit in the dashboard.

  • 03

    ACH payment fraud

    ACH payment fraud — fraudulent debits, R-code abuse, returned-item exploitation — handled via NACHA-aligned mandate evidence, R-code-aware retry policies, and merchant-side velocity caps on first-charge ACH authorisations.

  • 04

    Push-payment fraud

    Push payment fraud and authorized push payment fraud (APP fraud — the scheme term) on instant rails: beneficiary screening, name-matching where the rail supports Confirmation-of-Payee, and mandate-review steps for high-value or unusual flows.

  • 05

    Fraud online payment

    Fraud online payment generally — across cards, wallets, BNPL, bank rails and crypto — runs through the same signal model. Device intelligence, geo / BIN mismatches, behavioural-velocity signals and chargeback-history scoring feed the routing engine's risk axis.

  • 06

    Refund and chargeback abuse

    Refund abuse (false-decline followed by chargeback), friendly-fraud chargebacks, and serial-disputer patterns surfaced through chargeback-ratio analytics and per-customer history. Evidence-pack templates and automated representment reduce the operational cost of the legitimate ones.

How it works

From signal capture to operator review in five stages

What happens between an authorisation hitting the API and a suspect case surfacing on an operator's review queue. Five stages, most measured in milliseconds.

  1. 01

    Score every authorisation

    Device, geo, BIN, scheme, currency, velocity, list-membership and risk-engine-partner signals combine into a composite score on every authorisation — card, ACH or push.

  2. 02

    Route or challenge

    Low-risk authorisations route through the standard engine; mid-risk trigger 3DS2 step-up or beneficiary verification; high-risk reject before reaching the acquirer.

  3. 03

    Cascade with risk awareness

    Soft declines cascade to the next ranked acquirer only when the risk signal allows it — the cascade is risk-aware, not blind to fraud patterns.

  4. 04

    Capture evidence on settlement

    Vault tokens, network-token IDs, 3DS challenge outcomes, mandate IDs and device signals attach to every authorisation row for downstream evidence packs.

  5. 05

    Operator dashboard for review

    Suspicious authorisations queue for operator review with full context — vault-token history, device fingerprint, list-match reason, scheme-programme position.

Main use cases

Where payment fraud risk management earns its keep

Six merchant shapes that share the same orchestration layer but stress fraud controls differently — DTC, SaaS, marketplaces, bank-rail flows, travel, crypto.

  • DTC

    Direct-to-consumer brands

    Card-not-present payment fraud risk management across cards, wallets and BNPL — velocity rules tuned to the merchant's typical basket and shipping pattern, with 3DS2 step-up policies that don't break conversion on legitimate buyers.

  • SaaS

    Subscriptions and SaaS

    Subscription-specific fraud patterns (free-trial abuse, card-testing on tokenised cards, friendly-fraud chargebacks on long-tail subscribers) — handled via signup-side risk scoring and per-customer chargeback-history tracking inside the recurring engine.

  • Marketplaces

    Marketplaces and platforms

    Per-tenant fraud policies for marketplaces — each seller's risk profile tunable separately, with marketplace-level rules layered on top. Suspicious-listing flags and seller-side velocity caps available where the marketplace exposes them.

  • Bank rails

    Open-banking and instant-rail flows

    Authorised push payment fraud risk on Open Banking VRP, SEPA Instant, Faster Payments and PIX — Confirmation-of-Payee where the rail supports it, mandate review for first-time payees, and beneficiary screening against sanctions and known-fraud lists.

  • Travel

    Travel, ticketing and high-ticket

    High-ticket payment fraud analytics — chargeback-ratio tracking per scheme, dispute-evidence templates tuned to travel-specific scheme codes, refund-abuse pattern detection.

  • Crypto

    Crypto payments

    On-chain address screening against sanctions lists (handled by the connected partner crypto gateway), refund-flow rules for stablecoin and major-token receipts, conversion-on-receipt as a treasury hedge.

Platform features

Capabilities behind the payment fraud solutions surface

What the platform actually ships for fraud — beyond the general orchestration features shared with non-fraud traffic.

  • Unified velocity rules

    Per-merchant, per-segment velocity caps — amount, count, geography, BIN — applied uniformly across card, ACH and push rails.

  • List management

    Block / allow / watch lists keyed on vault tokens, BINs, email domains, IPs, device fingerprints and beneficiary identifiers.

  • Fraud-engine connectors

    Partner-agnostic connectors for Sift, Riskified, Forter, Kount, Signifyd and similar — same authorisation path, same webhook events.

  • 3DS2 / SCA orchestration

    Selective 3DS2 step-up tied to the risk score; PSD2-compliant in Europe without breaking conversion elsewhere.

  • Beneficiary screening

    Push-payment beneficiary screening, Confirmation-of-Payee where the rail supports it, mandate-review steps for high-value or unusual flows.

  • Scheme programme tracking

    Visa VDMP / VAMP / VFMP and Mastercard ECP / EFMP thresholds tracked per merchant with dashboard alerts.

  • Chargeback evidence packs

    Vertical-specific evidence-pack templates that auto-populate from the authorisation, vault and device-signal data.

  • Automated representment

    Automatic representment for select scheme-defined chargeback codes — the operator reviews only the cases that need judgement.

  • Refund-abuse analytics

    Per-customer refund propensity and chargeback-history scoring fed back into the routing-engine risk axis.

  • Sanctions screening

    OFAC, EU and UK sanctions screening at onboarding and on beneficiary changes; ongoing monitoring tuned per merchant.

  • AML monitoring

    Volume, vertical and geography-aware AML monitoring with operator-side alerting.

  • Audit log

    Operator actions, override decisions and policy changes logged with actor identity and timestamp.

Trust & compliance

Compliance posture for payment fraud risk management

Every authorisation runs through a single audited environment. Sub-merchants inherit the platform's posture rather than carrying separate certifications per fraud control or per provider.

PCI DSS Level 1
Annual on-site assessment plus quarterly ASV scans; sub-merchants inherit the posture for card traffic.
PSD2 / SCA
Selective 3DS2 step-up on the authorisation path; SCA-compliant exemptions applied where allowed and risk-justified.
FATF Travel Rule
Where the connected partner gateway supports the FATF Travel Rule on the relevant rail, the metadata is captured and surfaced in the reconciliation feed.
Sanctions & AML alignment
Sanctions screening on onboarding; AML monitoring tuned per merchant vertical and volume — including beneficiary-side screening on push rails.
Data residency
Regional data-residency options where regulators require it; EU-resident traffic stays in-region by default.
Licensed verticals only
Licensed gaming, regulated financial services and other compliance-bound verticals supported only where current operating licences exist. Grey and black-market verticals — and merchants whose business model relies on fraud-adjacent patterns — are out of scope regardless of integration shape.

Ready to tighten controls

Anti-fraud controls inside the same orchestration layer.

A 45-minute fraud-controls review covers your traffic shape, current chargeback position, the partner fraud engine that fits (if any), and the rules to switch on from day one. Sandbox parity for fraud and dispute scenarios out of the box.

Book a fraud-controls review Read the API docs

Frequently asked

Buyer questions about payment fraud on topropay

What buyers ask before committing — definitions, APP-fraud, anti-fraud solutions, partner engine integration, card vs ACH posture, analytics and migration.

  1. 01

    What does payment fraud cover on topropay?

    Payment fraud on topropay covers the controls and analytics that sit across the connected provider portfolio — velocity rules, list management, 3DS2 / SCA orchestration, fraud-engine connectors, beneficiary screening for push rails, scheme-programme threshold tracking, dispute-evidence packs and automated representment. It's not a separate product; it's a first-class part of the orchestration layer.

  2. 02

    How does the platform handle authorised push payment fraud?

    Authorised push payment fraud — APP fraud — affects instant push rails (Faster Payments in the UK, SEPA Instant in EU, PIX in Brazil, Open Banking VRP). The platform layers beneficiary screening, name-matching (Confirmation-of-Payee where the rail supports it), mandate-review steps for first-time or high-value payees, and sanctions-list checks on the beneficiary identifier.

  3. 03

    What does push payment fraud look like in practice?

    Push payment fraud typically involves a buyer authorising a transfer to a fraudulent beneficiary — the rail itself is functioning correctly; the deception is upstream. Detection therefore depends on beneficiary signals (known-fraud lists, sanctions hits, name mismatches) and behavioural signals (first-time payee, unusual amount, unusual timing) more than card-style velocity or BIN signals.

  4. 04

    Does topropay sell payment fraud solutions, or is it an orchestration layer?

    topropay is an orchestration layer with payment fraud solutions built into the surface — not a standalone fraud-only product. The merchant gets routing, cascade, vault, reconciliation and fraud controls under one contract. Merchants who already run a dedicated fraud engine connect it through partner-agnostic connectors and keep that relationship.

  5. 05

    What anti fraud solutions does the platform bundle?

    Anti fraud solutions bundled on the platform include velocity rules, list management, 3DS2 / SCA step-up policies, scheme-programme threshold tracking, refund-abuse analytics and automated representment. The merchant can extend these with a connected partner engine for transaction-level scoring (Sift, Riskified, Forter, Kount, Signifyd and similar).

  6. 06

    How do the fraud management solutions integrate with the merchant's stack?

    Fraud management solutions integrate through the same unified API as the rest of the orchestration. Velocity caps and list-membership rules are dashboard-level; partner fraud-engine connectors call the merchant's chosen engine inside the authorisation path; the webhook event model carries fraud-related metadata (risk score, list-match reason, 3DS challenge outcome) alongside the standard authorisation fields.

  7. 07

    What payment fraud analytics does the dashboard expose?

    Payment fraud analytics on the dashboard include per-merchant chargeback ratio, per-segment refund propensity, scheme-programme position (VDMP/VAMP/VFMP/ECP/EFMP) with threshold alerts, per-BIN approval and dispute curves, and per-customer chargeback history. All filterable by acquirer, method and time window.

  8. 08

    How do you prevent payment fraud at the authorisation stage?

    To prevent payment fraud at the authorisation stage, the platform composes a risk score per authorisation from device fingerprint, geo / BIN consistency, velocity signals, list-membership and (where present) the partner fraud engine's verdict. The score drives one of three outcomes: route normally, route with 3DS2 step-up, or reject before reaching the acquirer.

  9. 09

    What does payment fraud management look like operationally day-to-day?

    Payment fraud management day-to-day runs through the operator dashboard: review the suspicious-queue list, approve / reject the borderline cases, respond to chargebacks via the evidence-pack templates, adjust velocity caps when a campaign drives unusual traffic patterns. The platform handles the boring parts; the operator handles the judgement parts.

  10. 10

    Is card payment fraud different from ACH payment fraud in the policies?

    Yes. Card payment fraud and ACH payment fraud have different signal models — card lives in BIN, scheme, 3DS and chargeback timing; ACH lives in NACHA R-codes, mandate evidence and the return window. The platform exposes per-rail policies so merchants don't try to fit one model to both.

  11. 11

    How does the platform handle credit card payment fraud specifically?

    Credit card payment fraud handling combines per-BIN routing weights (some BIN ranges have known elevated fraud), Visa / Mastercard scheme-programme threshold tracking, selective 3DS2 step-up based on risk score, and refund-propensity analytics tied back into the routing engine. The merchant doesn't pick between approval and fraud control — the routing engine optimises both axes per transaction.

  12. 12

    What about fraud online payment for low-ticket high-volume merchants?

    Fraud online payment risk on low-ticket high-volume merchants is dominated by card-testing and enumeration attacks. The platform's velocity rules detect those patterns at the IP, device and BIN level; partner fraud engines add behavioural detection on top. The combination typically reduces card-testing exposure to near-zero without breaking conversion on legitimate traffic.

  13. 13

    How does payment fraud risk management scale as a merchant grows?

    Payment fraud risk management scales without merchant-side rebuilds. The same dashboard rules, connectors and dispute queue serve a 100k/month merchant and a 100m/month merchant — the difference is the policies the operator tunes, not the integration shape. Enterprise merchants typically add per-segment policies and a dedicated partner fraud engine; sub-100M-EUR merchants run with the platform's defaults.

  14. 14

    Can a merchant migrate their existing fraud engine to topropay without losing rule history?

    Most partner fraud-engine connectors run the merchant's existing engine inside the topropay authorisation path — the rule history, the model and the decisions all stay with the partner engine. The merchant doesn't migrate the rules; they connect the existing engine and let it score authorisations on the platform's call.

  15. 15

    How does the platform handle dispute representment across providers?

    Dispute representment runs through one queue across acquirers. Evidence-pack templates per vertical auto-populate from authorisation, vault and device-signal data; automated representment handles select scheme codes; the operator reviews only the cases that need judgement. Per-acquirer dispute timelines are abstracted into one consistent calendar in the dashboard.

Related

Related on the topropay platform