Real-time payments are changing how fraud is managed, and the implications are only starting to become clear. As settlement times compress towards instant, decisions that were once spread across systems and timeframes are being forced into a single moment — just as AI is making fraud faster, more scalable, and harder to detect.
Historically, fraud prevention has depended on the presence of time in the system. Transactions could be reviewed, escalated, or reversed before settlement, allowing businesses to rely on rules-based systems supported by manual intervention. That model was never precise, but it was workable because there was always a buffer built into the payment itself.
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Real-time payments soar by >50% in 2025
Real-time payment volumes grew by more than 50% year-on-year in 2025, with the global market on course to exceed $600bn by 2035. What was recently treated as innovation has quickly become the baseline. Instant rails such as FedNow, SEPA Instant and Pix have transitioned from pilot to default, with adoption in some markets approaching ubiquity — Brazil’s Pix now reaches the vast majority of the adult population.
At its core, fraud prevention has always been a balancing act. The objective is clear — allow legitimate transactions to pass while stopping fraudulent ones — but in practice, systems have relied on approximation.
This is where payments providers have traditionally earned their advantage. The ability to move closer to that theoretical ideal — approving as many legitimate transactions as possible while preventing fraud — has a direct and measurable impact on bottom lines to the tune of more than $300bn, according to Nuvei research. The difference is not incremental. For large merchants, even a one percentage point improvement in authorisation rates can translate into millions in additional revenue, often exceeding the direct cost of fraud itself. False declines, in that context, are not just an operational inefficiency. They are a form of hidden revenue loss.
Fraud strategy: never purely about risk management
It has been a lever for growth, with the most effective systems generating meaningful incremental value by reducing unnecessary declines without increasing exposure. That process has always depended on time. And as payments become instant, that dependency becomes harder to sustain.
The trade-off becomes harder to maintain as payments settle in seconds and funds cannot be easily recovered. Decisions that were previously distributed across systems and timeframes collapse into a single moment. The window in which fraud can be identified and contained is narrowing by design.
At the same time, the nature of fraud is changing. What was once a problem of pattern recognition has become one of adaptation. AI has reduced the cost of generating synthetic identities, scaling social engineering, and coordinating activity across accounts and markets. Fraud no longer follows stable patterns. It evolves in real time.
In that environment, the constraint is straightforward. A payment should not move faster than its risk can be assessed. As that assessment window compresses, fraud decisioning must operate within the transaction itself, rather than around it. Systems built on static rules and retrospective intervention struggle to keep pace with that requirement, not because they are poorly designed, but because they were designed for a different operating model.
The growing importance of infrastructure
This is where the conversation moves beyond speed or even fraud in isolation, and into the role infrastructure now plays in determining how effectively fraud can be managed. Real-time decisioning depends not just on access to data, but on the ability to learn from it continuously and apply those insights within the same latency constraints as authorisation.
What distinguishes more effective systems is not simply connectivity, but the extent to which they can interpret and act on the signals within each transaction. Every payment carries information about behavior, intent, and risk. The ability to capture that signal, update risk models in real time, and apply those learnings to subsequent transactions is what allows fraud controls to become more precise without increasing friction.
Fragmented architectures struggle in this environment. Data is distributed, feedback loops are slow, and learning is constrained by the boundaries between systems, markets, or payment methods. The result is that fraud decisions are made with partial context, often too late to influence the outcome or without enough accuracy to avoid unnecessary declines.
By contrast, more unified infrastructure creates a compounding advantage. The more volume that flows through a single system, the more effectively it can distinguish between legitimate and fraudulent behavior, refine its decisioning, and improve outcomes over time. In practice, this is where global payment platforms that operate across hundreds of markets and payment methods begin to show structural advantages, not through rules, but through the ability to continuously learn from transaction-level data at scale. In that sense, intelligence is no longer an overlay to fraud systems. It is what determines their effectiveness.
The advantage is no longer in how those trade-offs are managed, but in how quickly systems can learn and adapt to move closer to that ideal — approving legitimate transactions while preventing fraud in real time – in a constantly shifting environment.
Speed, alone, does not create efficiency
The continued move towards account-to-account and instant payment rails reinforces this dynamic. These systems remove friction, reduce cost, and expand access, but they also remove many of the safeguards that slower systems implicitly provided. Speed, on its own, does not create efficiency. It amplifies whatever sits behind it.
Real-time payments are not the source of the problem. They are the environment in which existing limitations become visible. As AI accelerates the pace and sophistication of fraud, and as settlement continues to compress towards instant, those limitations become harder to ignore.
The question is no longer how to manage fraud within the constraints of slower systems. It is whether the systems themselves are built to operate at the speed the market is moving towards.
Noam Grinberg, Chief Risk Officer, Global Enterprise, Nuvei
