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The financial sector has faced a reckoning in the past few years. Regulatory penalties have surged to historic highs, with banks worldwide paying billions in anti-money laundering (AML)-related fines. Meanwhile, global fraud losses are estimated to exceed $485 billion annually, underscoring a stark reality: even the most advanced financial institutions struggle to keep pace with fast-evolving fraud tactics. What once passed as “good enough” compliance and fraud prevention, with periodic audits, layered tools, and manual reviews, now leaves gaping vulnerabilities.
At the same time, customers expect instant onboarding, seamless access, and account security, while regulators demand airtight controls and auditable data trails. For most banks, these goals feel at odds: the stronger the security, the slower the customer experience.
However, the real challenge isn’t choosing between growth, fraud prevention, and compliance. It’s the decades of point solutions and siloed data. Fraud teams can’t see what identity teams know. Cybersecurity tools detect anomalies, but not intent. And with every department adding one safeguard after another, independent of each other, legitimate customers wind up facing repeated verification prompts and friction that erodes user experience.
The hidden cost of fragmented defenses
Most financial institutions didn’t willingly set out to create silos; they simply reacted to new threats and regulations by adding one safeguard at a time: New regulations required additional reporting systems. New fraud vectors led to the development of new tools. Over the years, those layers multiplied into a patchwork of point solutions that rarely share data or context.
The result is a fragmented security ecosystem that’s both expensive and inefficient. In a recent survey of U.S. enterprises, only 27% of organizations reported that fraud prevention was jointly owned by finance and security teams, underscoring how structural silos continue to contribute to blind spots.
Each isolated tool sees only a slice of the truth, creating blind spots that fraud teams must manually stitch together. Identity teams verify documents. Cybersecurity teams monitor network anomalies. Compliance teams audit reports after the fact. Yet the most sophisticated attacks span all three, exploiting the gaps between systems designed to work independently.
What’s missing isn’t more security, it’s shared intelligence. Without a persistent view of devices and behaviors, even the strongest fraud prevention programs operate reactively. The institutions leading the way are the ones breaking down those walls, replacing disjointed systems with unified intelligence that allows every team to see the same signals in real time.
From static layers to dynamic intelligence
For years, banks have relied on layered security models with each tool designed to detect a specific type of threat. The idea was sound in principle: stack enough safeguards, and fraud would have nowhere to hide. But in practice, those layers have become rigid and reactive — and too slow to stop AI-driven fraud.
Fraudsters are using AI to generate synthetic identities, spoof device fingerprints, and mimic human behavior at scale. In our recent survey of over 300 business leaders, we found that 41% of all fraud attempts were AI-driven, with financial institutions reporting the highest exposure at 54%. The implication is clear: static defenses can’t compete with adaptive attacks.
What banks and financial institutions need now are actionable, real-time signals. Device intelligence provides exactly that. By analyzing hundreds of device, network, and browser signals, it establishes a persistent, privacy-safe view of every visitor. This allows risk and fraud teams to instantly recognize returning devices, even when cookies are cleared or browsers are changed, and flag anomalies that legacy systems may miss.
Unlike traditional risk scoring, which focuses on isolated events, device intelligence empowers fraud, identity, and cybersecurity teams to operate from a single source of truth and customize the signals to their use cases — enabling them to identify trusted users faster, spot suspicious behavior earlier, and reduce false positives that frustrate customers.
The result isn’t just stronger protection; it’s also gaining the ability to evolve defenses in lockstep with emerging threats while maintaining a friction-free user experience.
Smarter compliance in an era of AI-driven fraud
Heightened scrutiny around AML and know-your-customer (KYC) obligations has put compliance teams under a microscope. Regulators now expect continuous, data-backed monitoring, not quarterly check-ins or manual reviews. The pressure to detect suspicious activity in real time is mounting, yet traditional systems struggle to deliver that level of visibility without overwhelming teams or slowing down legitimate users.
Complicating matters, AI-driven fraud is blurring the line between legitimate and synthetic behavior. Fraudsters can now automate identity creation, mimic device patterns, and exploit inconsistencies between a bank’s fraud and compliance systems. A single weak link, such as a delayed alert or a missed cross-session connection, can cascade into reputational damage, financial penalties, or both.
Device intelligence helps bridge that gap. By connecting device, behavioral, and network signals across sessions, institutions can uncover anomalies that static KYC checks miss. For example, multiple “new” accounts tied to the same device, sudden geolocation mismatches, or repeated failed login attempts from identical fingerprints can all signal potential money mule activity or identity misuse.
The result is a smarter compliance model: one that detects risk earlier, strengthens AML and KYC efforts, and keeps onboarding experiences fast for legitimate customers. Instead of choosing between speed and scrutiny, banks gain the ability to do both.
Building a future-ready fortress in banking
When it comes to financial account security, resilience has long been equated with resistance (e.g., higher walls, tighter controls, more steps between the user and their goal). But resilience today means something different: Financial institutions need to know exactly who’s coming in so they can stop the fraudsters without accidentally blocking real customers.
The institutions best positioned for the next decade of risk management are those rethinking the fortress itself. They’re replacing reactive barriers with adaptive visibility — a shared intelligence layer that empowers every team to make faster, more confident decisions.
With hundreds of device, behavioral, and network signals at their fingertips, they can trace connections across sessions, spot coordinated fraud rings before they escalate, and strengthen compliance automatically in the background.
See what’s possible with unified intelligence
As the pace of fraud accelerates, the advantage lies with institutions that see more and act faster. Device intelligence makes that possible by uniting teams, systems, and strategies around a shared source of truth.
To learn how a unified approach can strengthen security and accelerate growth, start a free trial or contact our sales team to explore how modern banks are using device intelligence to build resilience for the future.
