The Office for National Statistics (ONS) recently revealed how fraud has risen by 33% in England and Wales.
Why? Because modern fraud doesn’t knock. It slips quietly through the cracks. And those cracks are often created by the very systems meant to stop it.
Over the years, many organisations have built their fraud defences one point solution at a time: device fingerprinting here, two-factor authentication (2FA) there, maybe an addition of biometrics or velocity checks. But this patchwork approach creates data silos, accrues tech debt, slows response times and leaves fraud teams playing catch-up against increasingly sophisticated, AI-powered threats.
Fraud is a constant, evolving pressure. And with 43% of businesses reporting fraud growing faster than revenue, the old ways are simply not sustainable.
The problem with patchwork solutions
The fraud prevention landscape is riddled with fragmented tools, reactive approaches and blind spots. Despite the best of intentions, many organisations rely on outdated, point-in-time methods that are ill-suited for today’s dynamic fraud landscape.
And fraud no longer plays by the old rules. It unfolds across the entire customer journey, mutating with every new channel, payment method or customer behaviour pattern. A fraudster may test stolen credentials one day, then come back weeks later to exploit a weak link in the onboarding or refund process. These disjointed systems miss multi-step attacks and patterns that unfold over time.
Unfortunately, the tools themselves are part of the problem. Methods like 2FA or basic security questions are routinely bypassed by today’s fraudsters, who use everything from brute-force tools to deepfake-enabled biometric spoofing. Meanwhile, point solutions may solve for a specific problem, but together, create an unwieldy tech stack and obfuscate visibility into the bigger fraud picture
The result is poor visibility, slow reaction times and missed fraud signals. And the consequences are mounting. In fact, many businesses may be losing up to 5% of their revenue when factoring in operational inefficiencies, compliance costs and customer churn.
Fraud is also evolving with new technologies and behaviours. Recent data shows that fraudsters are increasingly targeting emerging payment methods, with digital wallets (48%) and cryptocurrency (30%) expected to be among the most targeted fraud frontiers throughout 2025. As synthetic identities, social engineering and AI-generated content become more prevalent, it’s harder than ever for businesses to distinguish real users from fraudsters in real time.
Traditional fraud tools struggle to keep up because they’re reactive by design, relying on static rules, siloed data or manual review processes. Worse still, the lack of context means even genuine users can get flagged, resulting in false positives and increased abandonment rates.
This approach leaves businesses stuck in a cycle of chasing fraud, reacting late and layering on more tools in the hope of plugging the gaps. What’s needed now isn’t accumulation but unification.
Why integration beats isolation
If fragmentation is the problem, integration is the solution. A unified fraud prevention stack doesn’t just plug the gaps, it eliminates them. By connecting and centralising information across the customer journey, it reduces the blind spots, lag times and conflicting signals that plague point solutions.
When fraud signals are analysed in context, businesses can spot threats earlier, reduce false positives and make faster, more accurate decisions. This stops more fraud but also improves approval rates and protects the user experience. Not to mention, machine learning (ML) and AI-based systems train themselves to improve performance as new patterns emerge, thus continuously digesting and refining capabilities to identify nascent trends.
And while many organisations have historically relied on a patchwork of tools to cover each threat vector, it’s becoming clear that more tools aren’t the answer. Better coordination is. A modern stack doesn’t need to come from a single vendor, but it does need to operate like a single, unified system. That means integrated data, shared intelligence and orchestration that supports real-time response, not after-the-fact analysis. While investment is rising, with 85% of organisations having increased their fraud prevention budgets, it’s crucial to highlight that spending must be strategic.
So, what does a modern fraud prevention stack actually look like? And how can organisations build one that’s unified, flexible and future-proof?
The anatomy of a modern fraud stack
1. Start with an audit of existing anti-fraud capabilities
Before moving forward, organisations must understand what they already have with a thorough evaluation of current tools and processes. A comprehensive review of existing systems, looking across fraud, IT, compliance and customer teams, helps identify blind spots, duplication and where real-time capabilities are lacking.
2. Use advanced technology
With AI-generated fraud evolving fast, machine learning is no longer optional, as it’s now the engine of a modern, responsive fraud stack. Crucially, these models learn continuously, so fraud detection also gets smarter with every signal.
The most effective systems combine blackbox AI, which excels at spotting complex patterns, with whitebox AI, which explains why a transaction was flagged in the first place. Understanding and using both methodologies is the best way to serve fraud detection capabilities. This transparency builds trust, reduces false positives and helps teams fine-tune defences faster.
3. Employ real-time behavioural monitoring
By continuously tracking how users interact with digital channels – clicks, typing speed, device changes, login times – organisations can establish a baseline of normal behaviour and quickly detect anomalies with real-time behavioural monitoring.
Moreover, behavioural biometrics help build a more nuanced picture of trust, allowing fraud teams to reduce false positives while keeping bad actors out. This continuous vigilance is especially powerful in spotting synthetic identities and AI-generated threats that may look legitimate on the surface but behave abnormally over time.
4. Data consolidation and intelligence
Data is only as powerful as your ability to connect the dots. A modern fraud stack breaks down silos, bringing together fraud signals, digital footprint analysis and threat intelligence into one shared layer. When device, behavioural, transactional and third-party data can be cross-referenced in real time, teams gain a 360° view of risk and act accordingly.
The key isn’t just collection, but correlation. True intelligence is about bringing all the signals together to understand the risk and the wider impact.
5. Automation that works across the stack
Speed matters in fraud prevention. But so does accuracy.
That’s where intelligent automation comes in. By building automated workflows that stretch across the entire stack, organisations can reduce manual reviews, accelerate responses and trigger appropriate actions based on real-time risk assessments.
These workflows shouldn’t be tied to a single point solution either, as a modern stack allows you to create rules and triggers that span the entire customer journey, ensuring complete coverage and security.
A modern defence needs a modern foundation
Many organisations have built their fraud defences one point solution at a time. But in today’s fast-moving threat landscape, that patchwork approach is no longer fit for purpose. As fraud tactics evolve, so too must our response.The strongest defences are built on connected data, unified systems and smart automation, all working together to spot and stop fraud before it strikes.
About the Author
Adrian Jenkins is Revenue & Growth Leader at SEON. SEON is the command center for fraud prevention and AML compliance, empowering thousands of companies worldwide to stop fraud without slowing growth. Our platform powers smarter risk decisions by enriching customer profiles, flagging suspicious behavior and streamlining compliance workflows, all in one place. Unlike competitors relying on stale, third-party data, SEON leverages 900+ proprietary, real-time data signals that go deeper and broader, resulting in unmatched coverage and higher precision.
