Feedzai Unlocks $9T Fraud Network, Arming All Banks with Collective AI

NEW YORK, NY – June 09, 2026 – In a significant move to democratize financial security, AI leader Feedzai today launched its IQ Score, a solution that opens the collective intelligence of its $9 trillion global transaction network to financial institutions of every size. Delivered through a single API and available on the AWS Marketplace, the offering marks a strategic pivot from isolated institutional defense to a collaborative, network-powered approach in the fight against financial crime.

The launch directly confronts what Feedzai calls the "Silo and Legacy Paradox," a persistent challenge where banks, particularly regional and mid-sized ones, are constrained by fraud detection models that can only learn from their own internal data. As criminal tactics grow more sophisticated, often leveraging AI and striking across multiple channels, this limited view has become an untenable vulnerability. Feedzai’s new product aims to level the playing field, offering pre-trained, network-validated AI models that promise to quadruple fraud detection from day one.

Leveling the Playing Field for Community Banks

For years, the most advanced AI-driven fraud prevention tools have been the domain of large, multinational banks with the vast data volumes and dedicated data science teams required to build and maintain them. This has left smaller regional and community banks at a distinct disadvantage, often relying on traditional, rules-based systems that are ill-equipped to handle the speed and complexity of modern fraud schemes.

Feedzai IQ Score is engineered to dismantle this barrier. By providing network-level intelligence through a lightweight API, it eliminates the need for institutions to supply massive historical datasets or endure lengthy model training periods. The company claims banks can move from integration to seeing tangible results in a matter of days, requiring as few as 15 data fields for certain use cases like faster payments. This rapid time-to-value is a critical selling point for institutions that lack the resources for a complete and disruptive overhaul of their existing risk infrastructure.

"Fraud has outpaced what any single institution can stop alone," stated Pedro Barata, Chief Product Officer at Feedzai, in the announcement. "Feedzai IQ Score puts an end to isolated defense by giving banks access to collective insights from across our entire network." The solution promises not only a 4x improvement in catching fraud but also a 50% reduction in false positive alerts compared to legacy systems. This dual benefit is crucial; it not only stops more financial crime but also frees up fraud investigation teams from chasing dead ends, allowing them to focus on genuinely high-risk activity.

The Power of the Network: AI and Federated Intelligence

The technological foundation of IQ Score represents a significant evolution in financial security strategy. Instead of a consortium model where raw data is pooled, Feedzai employs a federated intelligence architecture. This AI-native approach allows the system to learn from fraud patterns across its entire $9 trillion network without any sensitive customer data ever leaving an individual bank's secure environment.

Here’s how it works: the system analyzes transaction metadata and fraud outcomes locally within each institution. It then generates anonymized, aggregated risk signals—what some in the industry call a "privacy-preserving fingerprint"—that are shared back to Feedzai's central AI models. These insights continuously refine and improve the global models, which are then pushed back out to all participating banks, strengthening their individual defenses with the power of collective knowledge. This creates a virtuous cycle where the system becomes smarter and more effective as the network grows.

This capability has drawn praise from industry analysts. "Network fraud intelligence sharing is becoming increasingly important in the monitoring of fragmented fraud signals within the financial ecosystem," noted Philip Mackenzie, Senior Research Principal at Chartis. "We considered this capability to be a key differentiator of Feedzai's IQ Score solution, which combines real-time cross-institutional fraud insights and collective intelligence across a range of financial institutions."

Navigating Privacy in a Collaborative Defense

Perhaps the most critical innovation within Feedzai IQ Score is how it navigates the treacherous waters of data privacy and regulation. In an era governed by stringent rules like GDPR and CCPA, the prospect of sharing data, even for a common good like fighting fraud, is a non-starter for most financial institutions. Traditional consortiums have often struggled to gain traction due to these compliance and competitive concerns.

Feedzai's federated learning model is designed to be privacy-preserving by its very nature. By ensuring intelligence travels without the underlying sensitive data, it allows banks to collaborate safely and effectively. The company emphasizes that there is "no data trade-off" and its system is not a "give to get" model, meaning all clients benefit from the network's intelligence without being forced to contribute their own proprietary data in a way that could create a competitive disadvantage. This privacy-first stance removes a major psychological and legal barrier, encouraging broader participation and thereby strengthening the network for everyone involved.

From Integration to Impact: The Operational Reality

Beyond the advanced technology, the practical implementation of Feedzai IQ Score is designed for operational efficiency. The availability on AWS Marketplace streamlines procurement and deployment, while the API-first design simplifies integration with existing banking platforms. For IT and operations managers, the promise of modernizing their fraud defenses without a disruptive, multi-year overhaul is a compelling proposition.

The operational impact extends directly to the front lines of fraud prevention. With a claimed 50% reduction in alerts, fraud analyst teams can shift from a reactive, high-volume, low-accuracy workload to a more proactive and strategic posture. Early case studies, such as one with Novobanco, have shown promising results, including a significant increase in the detection of scams and mule activity—two of the most challenging fraud typologies to identify using traditional methods.

By packaging sophisticated AI and network intelligence into an accessible, non-disruptive, and privacy-compliant solution, Feedzai is not just launching a new product; it is challenging the established paradigm of financial crime prevention. This move reflects a broader industry shift toward collaborative, cloud-native security strategies, recognizing that in the digital age, no institution is an island.