Real-time payment fraud prevention solutions for banks include Vyntra, Feedzai, ACI Worldwide, and NICE Actimize.
As instant payments become widespread, banks and financial institutions face a fundamental challenge:
How do you stop fraud on irrevocable instant payments without slowing them down or degrading customer experience?
Payment rails such as SEPA Instant, Faster Payments, RTP, FedNow, and Pix leave no room for post-event recovery. Once funds move, they are gone. That reality changes what “real-time fraud prevention” actually means.
Many vendors claim to operate in real time for instant payments. In practice, their approaches vary significantly in:
- Where decisions are made in the payment flow
- How predictable latency really is
- Whether fraud is stopped before settlement or investigated after
- How much operational overhead is required
Understanding these differences is critical when selecting a fraud platform for instant payments.
This article compares four platforms, Vyntra, Feedzai, ACI Worldwide, and NICE Actimize, and explains how you can decide which approach best fits your strategy.
In this article
Comparison table of real-time payment fraud prevention solutions for banks
Dimension | Vyntra | Feedzai | ACI Worldwide | NICE Actimize |
Primary positioning | Dedicated real-time payments fraud prevention | Enterprise-wide fraud platform | Payments infrastructure–led fraud | Enterprise financial crime and risk platform |
Designed specifically for instant payments | Yes | No (supports multiple channels) | Partially (aligned with payment rails) | No (part of broader financial crime stack) |
Decision point in payment flow | In line, in the execution path | Orchestrated real-time scoring | Integrated with payments infrastructure | Orchestrated within IFM workflows |
Pre-settlement interdiction | Yes. Hard block before settlement | Possible, depends on orchestration | Yes, depending on configuration | Possible, often via orchestration |
Decision determinism | Deterministic | Variable | Semi-deterministic | Variable |
Typical latency characteristics | Sub-50 ms, predictable | Real-time but variable | Scheme-aligned, configurable | Real-time capable, less predictable |
Latency isolation for instant payments | Yes. Isolated by design | No – shared with other channels | Partial | No |
Primary fraud types addressed | APP scams, social engineering, mule activity, rapid multi-payment fraud | Transaction fraud, behavioural anomalies, cross-channel fraud | Transaction fraud, payment abuse | Scams, mule networks, ATO, AML-related fraud |
Scam-specific behavioural detection | Strong focus | Moderate | Limited | Strong (typology-driven) |
AI / ML approach | Pattern-based + AI models optimised for scams | Supervised ML + behavioural analytics | Rules + adaptive ML | Typology-based multi-model analytics |
Network / collective intelligence | No shared network (institution-specific learning) | Yes – federated network intelligence | Yes – global payments network | Yes – collective financial crime intelligence |
Explainability and auditability | High: reimbursement and audit-focused | High: mature investigation tooling | Moderate | Very high: compliance-led |
Human-in-the-loop learning | Yes, without scaling operations | Yes | Yes | Yes |
Operational complexity | Low to moderate | Moderate to high | Moderate | High |
What is real-time fraud prevention?
In instant payments, “real-time” can mean very different things. Some platforms:
- Score transactions in line, before settlement
- Sit alongside orchestration layers
- Rely on alerts, customer prompts, or post-authorisation controls
For regulators such as the FCA and EBA, and for payment schemes governing Faster Payments and SEPA Instant, the key distinction is:
Can the platform deterministically stop funds before settlement within the scheme’s time limits?
When a payment fails, customers will increasingly expect you to spot the issue and act immediately, especially for large or sensitive payments. The biggest challenge is whether your systems can handle more checks without slowing everything down.
Many banks are already close to the performance limits of their core systems, which makes them wary of anything that introduces delay or unpredictable processing during payment execution. In practice, “best” often means least disruptive. So, real-time fraud solutions must be lightweight, fast, and predictable.
Below is a breakdown of how leading platforms approach fraud prevention for instant payments:
Vyntra: Purpose-built fraud prevention for irrevocable payments
Vyntra is designed specifically for instant, irrevocable payment rails. Its fraud engine operates directly in the payment execution path, producing deterministic allow-or-block decisions (typically under 50 milliseconds) before settlement occurs.
Instant payments are isolated by design from other payment types. This avoids shared queues, variable latency, or contention with cards, wires, or ACH flows.
In addition to inline enforcement, Vyntra’s decisions are informed by real-time transaction observability. The platform has visibility into a payment’s execution state as it progresses through the instant payment flow, including timing, sequencing, and behavioural signals from initiation through authorisation. This allows fraud decisions to reflect not only whether a transaction is risky, but whether it is still interceptable within scheme time limits.
Vyntra enforces hard pre-settlement controls, meaning:
- Funds cannot leave the bank unless the transaction is explicitly approved.
- There are no alerts, nudges, or downstream recovery assumptions.
Key features
- Real-time transaction observability across the instant payment execution state
- True pre-settlement interdiction
- Predictable, regulator-safe latency
- Designed for APP scams, social engineering, and mule activity
- Pattern-based detection for:
- Smurfing
- Rapid multi-payment fraud
- Coordinated scam campaigns
- Explainable decisions for reimbursement and audit
- Human-in-the-loop learning without scaling headcount
Feedzai: Enterprise AI fraud platform with network intelligence
Feedzai is a broad enterprise fraud platform covering cards, payments, digital channels, and accounts. It applies supervised machine learning, behavioural analytics, and rules orchestration to score transactions in real time, including instant payments. Its federated network intelligence, TrustScore, aggregates anonymised fraud patterns across its global customer base.
Key features
- Advanced machine learning and behavioural profiling
- Network intelligence from multiple institutions
- Unified customer risk across channels
- Mature case management and investigation tooling
- Proven scalability in tier-1 banks
Note: Enforcement for instant payments depends on how Feedzai is integrated into the bank’s payment orchestration layer.
ACI Worldwide: Payments infrastructure–led fraud protection
ACI Worldwide integrates fraud controls directly into its payments infrastructure ecosystem. Fraud prevention is deployed within or alongside payment engines, using a mix of rules, adaptive machine learning, and network intelligence drawn from established real-time payment markets. Its strength lies in long-standing experience with payment schemes, clearing, and settlement.
Key features
- Tight alignment with payment rails and scheme rules
- Proven track record in real-time payment markets
- Integrated payments and fraud processing
- Flexible deployment (on-premise or cloud)
- Network intelligence from global payment flows
NICE Actimize: Enterprise financial crime and typology-based detection
NICE Actimize positions instant payments fraud as part of a broader financial crime framework, alongside AML, scam detection, mule networks, and account takeover. Its IFM platform relies on typology-driven models, entity risk scoring, and network analytics, with strong emphasis on governance, auditability, and workflow management.
Key features
- Deep regulatory and compliance expertise
- Typology-based multi-model detection
- Strong entity, relationship, and network analytics
- Collective intelligence across institutions
- Robust audit and regulatory tooling
Five questions to ask when choosing a real-time payments fraud platform
To evaluate architectural fit, financial institutions should ask:
- Do we need guaranteed pre-settlement interdiction? If yes, deterministic in-line execution is essential.
- How sensitive are we to latency and customer friction? Instant payments leave no buffer for unpredictable delays.
- Is APP and scam fraud our primary risk? Scam-driven fraud requires different signals than traditional transaction fraud.
- Do we want a dedicated instant payments fraud layer or a single enterprise platform?
- How mature are our fraud operations? Some platforms reduce operational load; others assume large teams and workflows.
Real-time payments fraud prevention FAQs
What is real-time payments fraud?
Real-time payments fraud refers to fraudulent activity on instant payment rails where funds settle immediately and cannot be recalled. This includes APP scams, social engineering, mule activity, and rapid multi-payment fraud.
Why is fraud prevention harder for instant payments?
Unlike cards or ACH, instant payments are irrevocable. There is no chargeback window, which means fraud must be stopped before settlement, often within milliseconds.
Are all “real-time” fraud systems the same?
No. Some systems score transactions in line before settlement, while others rely on orchestration layers, alerts, or post-authorisation controls. The difference is critical for instant payments.
What is APP scam fraud?
Authorised Push Payment (APP) fraud occurs when customers are manipulated into sending payments to fraudsters. It is one of the fastest-growing fraud types on instant payment rails.
Do regulators require pre-settlement controls?
While regulations vary, authorities such as the FCA and EBA increasingly expect banks to demonstrate effective preventative controls, particularly for consumer reimbursement and scam mitigation.
