As payments accelerate, fraud prevention is becoming central to operational resilience, helping institutions keep legitimate transactions moving.
For years, fraud prevention was treated as a defensive function. The job was to detect bad activity, stop fraudulent transactions, limit financial loss, and recover what could be recovered. It was important, but it was often viewed as separate from the broader question of whether payments continued to move.
Here’s the problem: that separation has stopped working.
As payment systems become faster, more digital, and more interconnected, fraud is increasingly a direct threat to operational resilience. Real-time payments, new payment rails, and sophisticated scams are narrowing the time financial institutions have to detect, decide, and respond. In many cases, the issue isn’t whether the core payment system is technically available, but rather whether the financial institution has the confidence to let a payment proceed.
That’s why fraud prevention has become a matter of payment continuity. A bank can have systems that are up, connected, and functioning, but still pause, restrict, or reroute transactions if the fraud risk becomes too difficult to assess in the moment.
“Fraud increasingly affects payment availability, not just financial outcomes,” says Bottomline cyber fraud expert Eric Choltus. “When confidence in the legitimacy of a transaction drops, banks may pause, throttle, or reroute payments even when core systems remain fully operational.”
For banks and non-bank institutions, that changes the mission. Fraud prevention is no longer only about stopping bad payments. It’s about keeping good payments moving.
Faster payments need faster decisions
The industry phrase “faster payments, faster fraud” has become familiar because it is accurate. Traditional payment systems gave institutions more time to review suspicious activity, investigate anomalies, and, in some cases, recover funds after a transaction was flagged. Instant and real-time payment networks have changed that dynamic.
“Instant payment networks do not offer that luxury,” Choltus says. “Fraud screening, interdiction, and investigation all have to happen in real time.”
It creates a delicate balance. Clients and businesses expect payments to move quickly. They also expect banks to protect them. But overly aggressive fraud controls can damage the experience by delaying legitimate payments, especially high-value or time-sensitive transactions.
“If a legitimate high-value payment is delayed unnecessarily because of a false positive, the impact on the customer can be significant,” Choltus says.
The challenge, then, is not simply to stop more transactions. It is to make better decisions earlier, with greater confidence and less friction. Institutions need the ability to quickly distinguish between suspicious and legitimate activity to protect the customer without slowing payment processing.
Payment confidence depends on more than uptime
Operational resilience is often associated with availability, redundancy, and disaster recovery. Those remain essential, but payment continuity now depends on another factor: trust in the transaction itself.
“Safe payment processing depends on confidence, not simply uptime,” Choltus says.
That confidence comes from many places. A bank needs to know that the customer is who they claim to be, that the payment intent is legitimate, that the beneficiary is trustworthy, and that the transaction has not been manipulated. When those signals are clear, payments can move. When they are fragmented, inconsistent, or missing, institutions may choose containment over exposure.
That decision can create friction in the short term. But the alternatives are worse: financial loss, reputational damage, regulatory scrutiny, and erosion of customer trust.
At this point, fraud prevention and resilience converge. A payment system can be technically functional yet operationally constrained if the fraud risk cannot be assessed quickly and consistently. In that environment, weak fraud controls become a continuity problem.
Scams such as account takeover (ATO), authorised push payment fraud, and impersonation attacks can disrupt operations even when systems themselves are working normally. The disruption comes from uncertainty. If an institution cannot determine whether a payment is safe, it may have to slow the process down.
The best fraud prevention starts before the payment
Modern fraud prevention needs to begin earlier in the payment lifecycle. By the time a payment reaches the rail, the opportunity to intervene may already be limited. The most effective programs gather upstream signals before the transaction is executed.
“The most effective programs identify risk as early as possible in the payment lifecycle,” Choltus says.
Those signals can include suspicious logins, unusual digital-session behaviour, changes to beneficiary information, device anomalies, entitlement changes, and other contextual clues. Each signal may not be decisive on its own. Together, they can help institutions understand whether a payment fits expected behaviour or deserves a closer look.
Layered controls are essential. Authentication, entitlement checks, velocity monitoring, payee validation, anomaly detection, and step-up authentication all play a role. The point is not to create a wall of friction around every transaction. It is to apply the right level of scrutiny based on risk.
That kind of layered approach helps institutions reduce fraud while preserving throughput. It also supports a better customer experience because legitimate activity can move with fewer unnecessary interruptions.
Response speed as competitive advantage
Fraud threats change constantly. New scams emerge, fraudsters adapt, and attacks shift across channels. For Choltus, one of the most important measures of a modern fraud program is how quickly an institution can respond to a new threat.
He describes this as the Fraud Response Cycle, or FRC: “the time between identifying a new fraud threat and implementing, testing, and validating controls that address it.”
That cycle includes investigation, root-cause analysis, risk assessment, policy evaluation, control design, testing, deployment, and ongoing monitoring. Some institutions can move through that cycle in weeks. Others may take months.
The difference is not always size. “I have seen large institutions that respond extremely quickly because they have invested in people, processes, and fraud operations,” Choltus says. “I have also seen large organisations struggle because their response remains highly reactive.”
Agility is important. Challenger banks may benefit from newer technology environments that are less burdened by legacy systems. But any institution can improve response time by defining processes, assigning ownership, and establishing action plans before an incident occurs.
Fraud response cannot be improvised every time a new threat emerges. Institutions need repeatable processes that enable them to move quickly from detection to control deployment, without sacrificing governance or accuracy.
Intelligence makes the difference
Behavioural intelligence is becoming a more important part of fraud prevention. Behavioural biometrics, for example, can help determine whether a user session appears to be conducted by the legitimate user by analysing interactions such as typing patterns, navigation behaviour, or mouse movements.
Those insights become more powerful when combined with transaction monitoring and other risk signals. A payment may look acceptable in isolation, but the session behaviour leading up to it may tell a different story.
Consortium intelligence is another valuable capability. Shared intelligence networks can help institutions assess unfamiliar beneficiaries, accounts or patterns before funds leave the organisation. That broader view can be especially important when fraud moves across institutions and payment rails.
Choltus also emphasises that fraud controls cannot remain static. Continuous tuning is now a core requirement. Rules, models, and thresholds need to evolve as fraud patterns change.
Strong governance matters with AI
AI is already reshaping fraud on both sides of the equation. Criminals are using AI to scale scams, refine impersonation attempts, and exploit weaknesses more quickly. “Fraudsters are using AI aggressively, but financial institutions can also leverage it to improve detection, investigation, and response,” Choltus says.
The strongest path is often incremental. AI can first support investigators by helping identify suspicious activity, prioritise cases, and surface patterns that may otherwise be missed. As institutions gain confidence, more advanced machine-learning models can help detect anomalies that traditional rule-based systems may miss.
But AI adoption must be aligned with risk appetite, governance requirements, and operational objectives. The goal is not automation for its own sake. The goal is better, faster, and more consistent fraud decisions.
