Most fraud detection tools were built for a different era. They look for unauthorized access, stolen credentials, or card testing. But scams are different: the customer initiates the payment themselves, convinced they’re paying a legitimate counterparty. The credentials are valid. The device is trusted. The only signal that something is wrong lives in the transaction context, beneficiary history, and behavioral patterns around the payment.
Vyntra’s Payment Fraud Prevention solution closes that gap. It monitors transactions across the full lifecycle—from initiation to settlement—and applies scam-specific detection models that surface authorised push payment (APP) fraud, romance scams, investment scams, and impersonation schemes before the payment completes. The goal is simple: stop the transfer while there’s still time to intervene.
What the Solution Detects
Scams leave traces. The challenge is that those traces rarely appear in a single transaction. They emerge across a sequence of events: a new payee added, an unusual transfer amount, a change in device behavior, or a beneficiary account with a short history or a pattern of rapid fund movement.
Vyntra’s detection surface covers the signals that matter:
Payee risk: beneficiary accounts with limited history, recent creation, or links to known scam networks
Behavioral anomalies: customers deviating from their established payment patterns, including first-time transfers to new beneficiaries at unusual amounts
Session context: payments initiated during high-pressure scenarios, repeated failed attempts followed by a successful transfer, or rapid changes to personal details before a payment
Cross-institution patterns: collective intelligence that identifies scam networks operating across multiple banks, so a beneficiary flagged elsewhere triggers scrutiny at your institution
The solution does not rely on a single rule. It combines these signals into a risk score that fraud teams can act on immediately. When payment activity indicates a potential scam, the system flags subsequent transactions, helping stop the attack before it causes significant harm. In many cases, even initial payments can be recalled before settlement.
How APP & Scam Detection Works
The detection process follows three steps:
Capture and enrich: Vyntra ingests transaction data and enriches it with contextual information about the beneficiary, the customer’s history, and external risk signals such as biometrics and device intelligence. This enriched data forms the detection surface.
Score in real time: Scam detection models evaluate each transaction against known scam patterns. The scoring happens sub-second, within the payment window, so alerts arrive before the transfer completes.
Act or hold: Your fraud team receives the alert with the reasons behind the score. They can hold the payment, contact the customer, or allow it to proceed. The decision stays with your institution.
This is not a black box. Every alert includes the contributing factors, so your analysts can explain to a customer why their payment was flagged. That matters when the customer believes they are paying a legitimate invoice or helping a friend in trouble.
Vyntra APP & Scam Detection Capabilities
Vyntra’s Payment Fraud Prevention capability is part of the Fincrime Platform, therefore can benefit from:
Real-time scam detection across payment rails, including instant payments where the window for intervention is seconds
Enriched transaction data that gives your fraud team context, not just a score
Collective intelligence that shares scam indicators across the Vyntra network of financial institutions
Detection models tuned for authorised push payment fraud, not just unauthorized
The solution works alongside your existing fraud stack. It does not replace your transaction monitoring system or your sanctions screening. It adds a layer of detection specifically for the scam problem that traditional tools miss.
Proof That Matters
The strongest evidence for this approach is the type of fraud it targets. Authorised push payment scams exploit the gap between what customers see and what the transaction actually does. A customer sees a legitimate invoice. The transaction moves funds to a mule account. Traditional fraud tools see a valid authentication and a normal payment. Vyntra sees the beneficiary history, the behavioral context, and the network patterns that indicate a scam.
That is the difference between detecting fraud and detecting scams. Fraud detection asks: is this person who they claim to be? Scam detection asks: is this payment going where the customer thinks it is going? The second question is harder, and it requires transaction intelligence that spans the full payment lifecycle.
Vyntra was formed from the merger of NetGuardians and Intix. That combination matters: NetGuardians brought financial crime prevention expertise; Intix brought transaction observability. The result is a detection capability that sees both the transaction and the risk—two capabilities, one intelligence layer.
Talk to the Team
If your institution is seeing rising scam losses, or if your fraud team is spending too much time manually reviewing authorised payments, Vyntra can help. Request a walkthrough of the Payment Fraud Prevention solution and see how it works against your own transaction data.
FAQs about APP & Scam Detection
How is scam detection different from standard fraud detection?
Standard fraud detection focuses on unauthorized access: stolen credentials, account takeover, card fraud. App scams involve authorised payments, where the legitimate customer initiates the transfer. Scam detection requires analyzing beneficiary risk, behavioral context, and cross-institution patterns—not just authentication signals.
Does the software block payments automatically?
Vyntra flags suspicious payments and provides the reasons behind the score. Your fraud team decides whether to hold, investigate, or release the payment. The solution gives you the intelligence to act within the payment window, but the decision stays with your institution.
Can it detect scams on instant payment rails?
Yes. The detection runs sub-second, which means it can flag a suspicious payment before an instant transfer settles. The window is short, but the enriched data and real-time scoring are designed for that constraint.
Sibos Miami - Sept 29 - Oct 1, 2026
Learn how banks are shifting from monitoring their IT systems to Transaction Observability, and what it implies for operational risk, resilience, and customer experience.
