As a financial institution researching real-time payments fraud, you may be dealing with the following challenges:
- Your current fraud detection relies on holding payments for review and updating rules after new fraud tactics emerge. But real-time transactions require decisions in seconds, leaving no time to investigate suspicious transactions.
- Your card, A2A transfer, and mobile payment systems are fragmented, leaving you without a single view of customer activity across payment rails. As a result, teams waste time switching between systems to gather context, allowing fraudulent transactions to slip through.
- Your internal teams and processes aren’t set up to handle real-time payment fraud. There’s no established operating model for instant decisioning, and ownership tends to float between Compliance, Security, and IT, leaving no single team accountable for stopping fraud.
At Vyntra, we work with over 130 financial institutions across 60+ countries that are experiencing similar challenges. To help you understand what you’re up against and how a purpose-built solution can help you combat real-time payments fraud, this article will cover:
Want a real-time payments fraud solution that helps you stay ahead of financial crime across payment rails? Get in touch with us to book a demo.
In this article
The key challenges of combating real-time payments fraud
Fraud in real-time payments is now 10x higher than in traditional payments. Here’s what makes it so difficult for banks to combat:
Real-time payments allow no time for review
With standard account-to-account transfers, you have time to spot and stop suspicious payments before the money leaves a customer’s account. Real-time payments, on the other hand, settle in seconds, leaving no opportunity for manual reviews.
For example, if a customer is tricked into sending money to a fraudster and only realises minutes later, the bank has no chance to intervene. By the time the fraud is reported, the payment has been settled, and the funds are already moving through a network of mule accounts.
You don’t have a unified view of your payments
In many cases, customer data and behavior are siloed across different payment rails, with separate systems for cards, transfers, and mobile payments. Without a full view of the customer’s activity across all these rails, you can’t see the full lifecycle of an attack, and fraudulent payments can slip through undetected.
For example, a fraudster might attempt a small test transaction on a customer’s card to verify access to their account. Because the card system and real-time payments system don’t share data, nothing connects that test transaction to the large real-time payment that follows shortly after. And in the seconds it takes for a real-time payment to clear, the customer is drained of their funds.
Real-time payments are driving a surge in APP fraud
The instant nature of real-time payments has also led to a surge in Authorized Push Payment (APP) fraud, where bad actors manipulate customers into authorizing payments through romance scams, impersonation, or fake invoices.
In fact, APP fraud losses are expected to reach nearly $7.6 billion by 2028 across six major markets, including the US, UK, India, Brazil, Australia, and Saudi Arabia. That’s because, by the time a customer realizes they’ve been scammed, the payment is irrevocable and the funds have already reached the fraudster’s bank account.
Regulatory pressure and reputational risk are increasing
As real-time payments fraud grows, so does your exposure to fraud risk and the scrutiny you face from regulators.
Across financial services, regulators are introducing stricter requirements around consumer protection and fraud liability. For example, in the UK, banks are already required to reimburse APP fraud victims up to £85,000 per claim under rules introduced by the Payment Systems Regulator in October 2024. And, in the EU, incoming PSD3 payment services legislation will hold banks directly liable for fraud losses if they fail to implement adequate prevention measures.
At the same time, the reputational consequences can be just as damaging. Customers who lose money to fraud and feel their bank failed to protect them are unlikely to stay. And when cases attract negative press coverage, the damage extends further, making it harder for banks to retain existing customers and attract new ones.
What to look for in a real-time payment fraud solution
When evaluating solutions, it helps to focus on the capabilities that enable you to operate effectively in a real-time environment. Here are key questions to ask:
Is it specifically built for real-time payments?
Faster payments infrastructure is built around speed, and every component, by mandate, must keep pace. To make accept-or-reject decisions in seconds, you need a solution designed specifically for real-time. This ensures your teams can assess transactions quickly and block suspicious transfers, while staying compliant with regulatory requirements.
Does it combine AI-driven anomaly detection with pattern-based intelligence?
An effective solution uses AI and machine learning risk scoring to review each transaction and compare it with a customer’s usual behavior. But strong detection goes further than spotting behavioral deviations.
Look for a solution that also recognizes known fraud patterns. For example, a sudden spike in payments combined with transfers to multiple new beneficiaries is common in social engineering attacks, where a victim is manipulated into making multiple payments in quick succession before realizing they’ve been scammed.
It’s also worth considering what data the solution draws on. Reliable systems don’t rely solely on transaction data. They also incorporate network risk scores, device intelligence, and shared fraud intelligence. This broader view helps build a clearer picture of suspicious activity and maintains accurate detection, even as fraud tactics change.
Will you have support to align the solution with your operations during setup?
The technology is only part of the equation. Without hands-on onboarding support, teams and workflows can fall out of sync with a new system, slowing your time-to-value. Look for service providers that helps you structure your teams and design processes around the solution, so it fits seamlessly into your existing operations.
Does it offer a full view of the customer across different payment rails?
To detect and block real-time fraud, you’ll want a solution that provides visibility across different payment rails. In that way, teams can investigate and act without having to jump between systems. Look for a solution that brings investigation and case management tools together in a single environment, so teams can review alerts and take action quickly.
Why choose Vyntra’s real-time payment fraud solution
To operate effectively in a real-time environment, you need a fraud detection system that combines instant decisioning, behavioral analysis, and cross-rail visibility. Available as a SaaS solution or an on-premise add-on, Vyntra’s real-time fraud solution delivers precise fraud prevention without compromising speed or customer experience. Here’s what you can expect if you partner with Vyntra:
Combine behavioral intelligence and pattern recognition to catch fraud before funds leave a customer's account
Detecting and blocking real-time payments fraud is challenging when you rely on rule-based systems that were built for slower, traditional payment rails. Vyntra’s solution is purpose-built for real-time, meaning:
- Transactions are analyzed in around 50ms at 100+ transactions per second, so fraud decisions happen within the strict time limits of instant payment networks.
- AI-driven anomaly detection spots both unexpected customer behavior and known fraud patterns, so your teams aren’t limited by manual, static rules that generate false positives and slow investigations down.
- Each transaction is analyzed in context, factoring in transaction history, payment velocity, new payees, and beneficiary rotation patterns commonly associated with fraud attacks. These are then evaluated against each customer’s behavioral baseline rather than a generic threshold. That way, suspicious activity is caught across multiple payments rather than in isolation.
- Detection draws on multiple signals, including transaction data, network risk scores, device signals, and shared community intelligence, giving your teams a complete picture in one place to streamline fraud investigations.
For example, let’s say a fraudster tricks a customer into making multiple payments to new beneficiaries in quick succession in a social engineering attack. Our solution detects the spike in payment velocity and the rotation of new beneficiaries. It then evaluates those signals against that customer’s baseline behavior, flags the pattern, and blocks the payments.
Get a single view of customer activity across all payment rails for faster, more accurate fraud decisions
When fraud teams rely on multiple disconnected systems, decisions slow down and suspicious payments slip through. Vyntra brings fraud detection across all major payment types including cards, A2A, and mobile payments into a single platform, giving your teams a complete picture in one place.
Instead of piecing together activity across systems, your teams can see how transactions across different rails relate to one another. Our AI and pattern-based intelligence automatically spots suspicious connections, enabling faster, more confident accept-or-reject decisions.
Take the card testing scenario described earlier. The test transaction and the subsequent large real-time payment are both visible in a single view. Combined with additional risk signals like an unfamiliar device or unusual location, the system flags the pattern and blocks the payment before funds leave the account
Combat real-time payments fraud with a purpose-built solution built around your teams and processes
Real-time payments require instant accept-or-reject decisions, which means you need a new approach to fraud defense. Getting it right demands the right solution, processes, and internal ownership, which is why Vyntra combines a prebuilt real-time fraud detection solution with expert-guided advisory support.
Our experts work closely with your organization to ensure the solution is implemented effectively. Shaped by over 13 years of experience across multiple markets, we’ll help you establish clear ownership, define decision frameworks, structure fraud teams appropriately, and integrate the solution into your existing operations.
Our solution comes with the AI models, infrastructure, and case management tools you need to combat real-time payments fraud. It’s continuously maintained and updated to reflect the latest fraud tactics, so you’re never left managing that burden internally. This gives you the clarity and accountability needed to make consistent, confident fraud decisions at speed
How one European bank moved to real-time fraud decisioning with Vyntra
A mid-sized European bank expanding its instant payments offering needed to shift from hold-and-review fraud controls to real-time decisioning.
But with no clear ownership across fraud, compliance, and IT, limited fraud data to benchmark performance, and a hybrid architecture split between real-time and legacy systems, making that transition proved challenging.
By embedding Vyntra’s solution directly into its real-time payments stack, the bank was able to score transactions within strict latency constraints without disrupting existing flows. Shared community intelligence provided early benchmarks where internal data was lacking, while Vyntra worked closely with the bank’s teams to define ownership, decision thresholds, and investigation workflows.
As a result, the bank moved from a fragmented approach to a structured real-time fraud capability. Detection accuracy improved, false positives decreased, and teams were able to make faster, more consistent decisions as instant payment volumes continued to grow.
Choose a purpose-built solution that provides full context and visibility for more effective fraud prevention
Real-time payments remove the window you once had to investigate suspicious transactions before funds left a customer’s account.
Look for a solution that detects both behavioral anomalies and known fraud patterns. Beyond the individual transaction, it should draw on transaction history, community intelligence, network risk scores, and device signals to build a complete picture of every payment. And it should bring all of this together in a single environment across every payment rail, so your teams have the full context and visibility they need to detect and stop fraud before funds leave a customer’s account.
It’s also worth considering whether you’ll have help structuring your internal setup around the solution. That way, you can be confident it fits the way your teams operate, and they can start tackling real-time payments fraud from day one.
If you’re looking for a real-time payments fraud solution that helps you reduce fraud losses, maintain customer trust, and meet regulatory expectations without sacrificing speed or customer experience, book a demo with us today.
FAQs - Real-time payments fraud
What is the difference between APP fraud and other types of real-time payments fraud?
Authorized Push Payment (APP) fraud is different from other types of real-time payment fraud because victims are manipulated into authorizing payments themselves, typically through romance scams, impersonation, or fake invoices. Meanwhile, other fraud often involves unauthorized access through weak or compromised authentication, such as hacking or the use of stolen credentials.
How quickly can a real-time payments fraud solution be deployed?
How quickly a real-time payments fraud solution can be deployed depends on the solution itself. A prebuilt, out-of-the-box solution like Vyntra’s, with a well-documented API, can be deployed in weeks. A custom build, on the other hand, can take months due to the complexity of building and tuning models, infrastructure, and case management tools from scratch.
What types of fraud occur in real-time payments?
The types of fraud that occur in real-time payments include APP fraud, account takeover, hybrid attacks combining social engineering and malware, smurfing and micro-transaction fraud, phishing and smishing scams, and session hijacking. Of these, APP fraud is the most common and the fastest growing.
Why are banks’ internal operations often unprepared for real-time payments fraud?
Banks’ internal operations are often unprepared for real-time payments fraud because their fraud operations are designed around slower payment processing models, where fraud teams have hours or days to hold, investigate, and reverse suspicious payments. However, with real-time payments, that window falls away entirely. Decisions have to be made in seconds before funds are transferred from a customer’s account. And most banks don’t yet have the operating models, decision frameworks, or clear internal ownership structures to support this new way of working.
