How to Stop Scams with Signal Convergence

Criminals have shifted from directly cracking accounts to coercing account holders through scam attacks. This pivot has two critical outcomes. First, it makes customers an integral part of the scam. And second, it renders traditional fraud prevention solution warnings ineffective. Fraudsters’ shift in tactics should prompt financial institutions to rethink their fraud strategy from point solutions that address a single challenge to a signal convergence-based approach that combines device intelligence, behavioral biometrics, and malware activity to deliver a unified threat intelligence assessment before scam losses occur. 

This article will guide you on how signal convergence works and the steps your organization can take right away to get started.

Key Takeaways

• Because the scams are committed by legitimate account holders who are being manipulated, they are rarely flagged by a single signal. Scam indicators become clearer when multiple signals are viewed together. 
• Signal convergence combines device, behavioral, and malware signals, enabling a shift from a micro to a macro view that considers all signals simultaneously.
• This approach shifts from focusing on device anomalies to behavioral shifts from coercion that show up when correlated with session patterns, transaction context, or timing changes.
• By using signal convergence, banks can achieve faster fraud detection, better trade-offs between detection and customer experience, and a more precise ROI gauge.
• Signal convergence can also help financial institutions detect more fraud without stopping good customers from transacting; new signals can be introduced without reconfiguring an entire technology stack.

Why Financial Institutions Need to Reassess Their Scam Prevention Strategy

It wasn’t that long ago that the fraud prevention playbook followed four simple steps:

1. Spot the suspicious device
2. Flag unusual behavior
3. Shut down known malware
4. Verify the location

This strategy worked well against account takeover fraud. When it comes to scams, however, those methods don’t translate into protection. That’s because, in the case of scams, the “fraudster” is the genuine account holder, but they are under the influence of a bad actor.  As a result, scams do not trigger the same flags as an ATO attack. Most of the time, the login looks normal, and often the device is clean. 

Even though the device and location appear normal in scams, scam behavior introduces subtle inconsistencies such as hesitation patterns, guided input sequences, remote-access artifacts, or step-order deviations. These weak signals don’t rise to the threshold of risk individually. But when combined in a unified engine, their relationship becomes visible, revealing the underlying coercion.

This is why some banks are re-examining their signal stacks. Each vendor has a niche, such as device fingerprinting, bot detection, or behavioral analytics, which banks stitch together. That model worked when fraud patterns were slower to evolve. But now, maintaining a patchwork of point solutions can result in:

• Cost Overhead: Every additional platform comes with licensing fees, integration costs, and maintenance.
• Operational Complexity: More tools lead to increased dashboards, vendor management, and internal alignment, causing fraud teams to reconcile differences instead of acting on insights.
• Conflicting Signals: Conflicting risk assessments between systems lead to manual review, delaying responses, increasing false positives, and missing fraud, thus burdening analysts.

When done right, consolidating vendors can help FIs cut costs, streamline decision-making, and improve fraud detection. It also results in signal convergence, bringing together multiple insights into an actionable, risk-based assessment. 

What Is Signal Convergence? A New Approach to Scam Detection

What does a scam look like from a fraud signal perspective? 

On the surface, everything will appear normal. The user accesses their account on their normal device and in their normal location, using their normal login credentials: no obvious suspicious activity or red flags.

However, abnormal activity is occurring just beneath the surface, seemingly normal activity that indicates a scam is underway. This may include slower typing speeds, delays in confirming information or identity, or different navigation methods (e.g., web instead of mobile, order of pages visited, session duration, or idleness). It can also be accompanied by unusual behavior, such as adding a new payment recipient, making a high-value transfer, or an abnormal payment chain. There may also be indications of malware or remote accessing tools (RATs) at work. 

Only a converged system can identify all of these different events as a single pattern. Signal convergence isn’t just a procurement strategy, it’s a chance to completely change how banks think about risk. Especially when it comes to scams.

When device, behavioral, malware, and threat intel signals are handled separately, they produce isolated “micro-decisions.” These can’t produce the real insights you need, and certainly not simultaneously. One system makes a call based on the device. Another system weighs in on behavior. A third adds threat intel. But it’s up to the analyst to stitch these together, and analysts are already under a great deal of pressure. Ask them to do this in real time, and you’re asking them to fail.

“Advanced solutions drive signal convergence by merging behavioral, device, transactional, and contextual signals into a resilient, high-fidelity data layer. This convergence is essential for more accurate fraud detection and produces measurable improvements in performance and ROI across fraud systems and strategies.” — Stuart Dobbie, Sr. Product Director for Digital Trust, Feedzai

If you bring those same signals into a single risk engine, you don’t just have intel, you have intelligence. That shift from micro view to a macro view allows the system to weigh all signals together, understand how they interact, and produce a single, more accurate outcome.

That’s a game-changer. Suddenly, this is what becomes possible:  

• Faster Detection: Identifying new fraud patterns immediately, since the engine sees the full picture from day one.
• Better Trade-offs: Tuning thresholds with confidence, knowing how every signal affects both detection and customer experience.
• Precise ROI: Measuring ROI exactly: tie a 1% drop in model performance directly to expected losses and weigh it against your contract cost.

This is also where resiliency comes in. Fraudsters go where the defenses are weakest. If you remove a control because “it doesn’t add value,” you might just be removing the very deterrent that kept them away. A converged signal system lets you adjust, experiment, and optimize without leaving gaps that criminals are sure to find.

Strategic Signal Convergence Recommendations for Banks

Signal convergence creates risk insight not because each signal is individually anomalous, but because the relationships between normal-looking signals reveal scam behaviors. Only a sophisticated signal convergence solution can reveal the threats happening beneath the surface.

But the way you get there matters just as much as the decision to do it. Here’s how to approach it without introducing new blind spots or wasting money.

1. Unify Signals in a Single Risk Engine

Bringing device, behavioral, malware, and threat intel into one decisioning layer allows you to make better risk decisions.  

• A single risk engine can see how signals interact and resolve conflicts automatically, instead of leaving it to an analyst under time pressure.
• Choose a platform or architecture that can ingest multiple feeds, weight them appropriately, and update models in near real time.

2. Measure ROI for Every Signal

Every feed has a cost, and every feed should be tied to a measurable outcome.

• Remove guesswork by testing model performance with and without each signal.
• If dropping a feed reduces performance by 1%, translate that into projected fraud losses in dollars. Compare that to the contract cost.
• Keep a living ROI scorecard so budget discussions are based on numbers, not opinions.

3. Keep Redundancy Where It Counts

It’s important to note that fraudsters are creatures of habit and are drawn toward the weakest link in defenses. For this reason, some controls do not exist solely for detection accuracy. Many fulfill compliance obligations or serve as quiet but essential deterrents. When a control appears to add “no value,” that often reflects the fact that it has successfully discouraged attacks in that space, not that the risk has disappeared.

• Instead of removing controls outright, validate your coverage.
• Implement a resilient signal strategy that maintains safeguards that matter 
• Remember that these signals may not always fire, but that’s because they keep fraud from showing up in the first place.

4. Build for Continuous Change

Signals that are valuable today might not be tomorrow. Privacy rules change, OS updates roll out, and fraud tactics evolve. Ensure you select a vendor platform capable of scaling to meet your organization’s future growth objectives.

• Assign dedicated R&D resources to monitor technical standards, browser/OS changes, and fraudster forums.
• Have a process to introduce new signals quickly without re-architecting your entire stack.
• Ensure your platform partner offers architecture that can grow, scale, adapt, and adopt new scam signals as new tactics emerge.

5. Factor in Customer Experience from the Start

Signal convergence allows you to catch more fraud and to do it without stopping good customers from transacting. 

• Use converged signals to reduce unnecessary friction for trusted customers.
• Prioritize passive authentication methods where possible, so legitimate transactions flow without extra prompts.
• Test friction changes not just for fraud reduction, but for impact on completion rates and customer satisfaction.

6. Treat Consolidation as a Design Project, Not a Procurement Exercise

Cost savings are a benefit, but they shouldn’t be the only driver.

• Map your current signal coverage and identify true overlaps versus complementary feeds.
• Pilot consolidations before committing fully, to confirm there’s no hidden coverage loss.
• Plan for an exit strategy so you know how to reintroduce a capability if the consolidated setup underperforms.

The Future of Fraud Prevention: Building a Resilient Signal Strategy

Fraud prevention has always been a cat-and-mouse game. What’s different now is how fast the game changes. It’s not just because of criminals, but because of the technology and rules that shape the signals we rely on.

The pace of change isn’t going to slow down. Criminals are innovating, vendors are merging, and privacy rules are in a state of flux. The banks that come out ahead will be the ones that treat fraud prevention as a living, evolving capability, one designed for speed, resilience, and the reality that the next big threat is already in motion.

Now is the moment to step back, cut through the noise, and design a signal strategy built for the world we actually operate in, not the one we wish we still had.

Additional Resources

• Blog: What is Device Intelligence & How Does it Work?
• Webinar: The Future of Fraud is Here: Are You Ready?
• Report: Shift from Scams to Trust with AI: A Guide for Banks
• Solution: Bank Account Opening Without the Friction