by Andrew Jolley, CAMS, Enterprise Fraud Solutions Strategist, FiVerity
Detect → Investigate → Report → Repeat.
Financial institutions have poured billions into advanced detection tools like AI, machine learning, transaction monitoring systems, and behavioral analytics. Yet the uncomfortable reality is that these solutions often activate only after fraud has taken place. Often times an alert is triggered, and a case is investigated, the damage is done, the fraudster has vanished, and the institution bears the loss.
This model has locked institutions into a dangerous loop, one that is driven by reaction rather than prevention. Detection catches just enough to manage the fallout. Investigations race to limit financial and reputational damage. Reporting satisfies regulatory requirements. But then what? The process resets, and the next attack begins.
To truly break the cycle, we need to move beyond isolated responses and toward a collective, coordinated approach to fraud prevention. The missing piece of the puzzle? Real-time Collaboration and shared intelligence between financial institutions. Without shared intelligence across institutions, each organization is left to fight the same battle alone, reacting to threats others may have already seen. Such as missing critical data points like mule accounts, synthetic identities, known fraudulent devices, and compromised credentials that others may have already seen.
The Limitations of Detection-Centric Strategies
Let’s be clear: detection is essential. But it's no longer sufficient on its own.
Fraud typologies are evolving faster than individual institutions can adapt. Sophisticated networks of cybercriminals are sharing tools, tactics, and compromised data in real time while most financial institutions are left to operate in silos, isolated by internal policies, technological limitations, and legal uncertainty.
Even the most advanced detection systems still face critical limitations:
- They work in isolation. A fraud attempt may be flagged at one institution, but the intelligence rarely reaches others in time to stop related attacks.
- They act too late. Real-time detection often still means post-event analysis. The loss has already occurred.
- They generate alert fatigue. Analysts are overwhelmed by false positives, slowing response times and increasing the risk of missing actual fraud.
To break out of this cycle, we need to rethink the foundational assumptions of fraud prevention. We need to move beyond detection.
From Isolated Alerts to Shared Intelligence
Fraud doesn’t happen in a vacuum. It’s often part of a broader pattern across institutions, geographies, across industries. But these patterns are nearly impossible to see when each organization is looking through a keyhole.
By securely and anonymously sharing intelligence across institutions, we can:
- Identify fraud earlier, by spotting repeat attempts across multiple organizations.
- Prevent fraud proactively, using real-world data from similar institutions to flag emerging threats.
- Coordinate responses, enabling faster action and improved defense against coordinated attacks.
This isn’t just theory, it’s already happening. FiVerity’s Anti-Fraud Collaboration Platform enables secure, anonymized fraud intelligence exchange between financial institutions, regulators, law enforcement, and technology providers. The result is a community defense model that makes everyone stronger together.
At FiVerity, we believe the future of fraud prevention isn’t just about adding more detection tools, it’s about connecting the tools that already exist through collaborative, real-time intelligence sharing. Using real-time collaboration as a force multiplier within your fraud risk framework, you will unlock a new level of Defense.
Real-Time Collaboration: A New Layer of Defense
Real-time collaboration enables financial institutions to move beyond siloed investigations and into a shared ecosystem of intelligence. By exchanging anonymized fraud signals and typologies across trusted networks, organizations can identify threats earlier and respond faster. This approach adds a powerful layer of defense, complementing internal controls with collective insights from peers, partners, and regulators. It transforms fraud prevention from a reactive task into a proactive, community-driven strategy.
Here’s what that looks like in practice:
- Anonymized intelligence sharing protects privacy while enhancing threat visibility.
- Automated categorization of fraud types to streamline investigation and pattern recognition.
- Transparent risk scoring helps institutions prioritize threats based on community-wide intelligence.
From Isolated Alerts to Shared Intelligence
Fraud doesn’t happen in a vacuum. It’s often part of a broader pattern across institutions, geographies, across industries. But these patterns are nearly impossible to see when each organization is looking through a keyhole.
By securely and anonymously sharing intelligence across institutions, we can:
- Identify fraud earlier, by spotting repeat attempts across multiple organizations.
- Prevent fraud proactively, using real-world data from similar institutions to flag emerging threats.
- Coordinate responses, enabling faster action and improved defense against coordinated attacks.
This isn’t just theory, it’s already happening. FiVerity’s Anti-Fraud Collaboration Platform enables secure, anonymized fraud intelligence exchange between financial institutions, regulators, law enforcement, and technology providers. The result is a community defense model that makes everyone stronger together.
At FiVerity, we believe the future of fraud prevention isn’t just about adding more detection tools, it’s about connecting the tools that already exist through collaborative, real-time intelligence sharing. Using real-time collaboration as a force multiplier within your fraud risk framework, you will unlock a new level of Defense.
By enabling financial institutions to work together we can change the economics of fraud. We can make attacks more expensive, more visible, and less effective. And we can start preventing fraud before it occurs. Fraud doesn’t happen in a vacuum. It’s often part of a broader pattern across institutions, geographies, across industries. But these patterns are nearly impossible to see when each organization is looking through a keyhole.
Breaking the Loop
Detection will always have a role to play. But if we want to end the cycle of detect → investigate → report → repeat, we must add a new step: collaborate.
It’s time to move from isolated systems to connected ecosystems. From reactive case management to proactive intelligence networks. From silos to shared defense.
Fraudsters are working together. It’s time we did it, too.
Let’s break the cycle—together.