Shared Network Intelligence Adds Ecosystem Visibility to AI Models
Fraudsters are collaborative. They open mule accounts at one bank and cash out at another, and they exploit gaps between systems that don’t share intelligence with each other. But most banks still detect fraud on their own, and with instant payments, fraud teams now have seconds to decide if a transaction is fraudulent or not.
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Banks ask whether a transaction is unusual for this customer, but they rarely ask whether the counterparty is risky elsewhere.
CISOs and fraud practitioners are now rethinking their approach using network intelligence signals. Network intelligence shifts the lens by focusing on relationships across institutions. It maps how accounts, devices and identities connect.
“Rules-based systems told you what happened. Machine learning told you what was anomalous. Network intelligence tells you who is connected to whom, and that is a fundamentally different question,” said Sam Abadir, research director at IDC. “That is not an incremental improvement. That is a new detection surface.”
Anurag Mohapatra, director of product marketing and fraud strategy with NICE Actimize, said network intelligence can really make a difference in real-time payment fraud. For example, a mule account may be new to one bank but already reported at others.
“Single-bank models ask: Is this unusual for my customer? Network intelligence asks: Is this receiving account flagged as fraudulent at other banks?” Mohapatra said.
Evolution or Architectural Shift?
Not all experts see it as a structural change. For Trace Fooshee, strategic advisor at Datos Insights, network detection is part of a longer expansion of context. Fraud systems moved from transaction payloads to device data and geolocation. Networks are the next layer in that progression, he said.
“The push for more context continues to push outwardly and is not likely to stop with network and consortia signals,” Fooshee said.
Network intelligence extends artificial intelligence tools, but doesn’t redefine it. Many banks are implementing it as an added signal, not a replacement system.
“It is not about choosing between proprietary models versus network intelligence. It is about layering the defense,” Mohapatra said.
Shared intelligence adds context, but proprietary models still drive decisions. But operational adoption has been slow. Pilot programs are common. Live decision integration is harder.
“Measured by live decisioning integration rather than deployment, our conversations with bank-side practitioners suggest fewer than a third of Tier 1 institutions are there yet,” Mohapatra said. “Governance, data architecture and risk appetite all have to align before network signals touch live decisions, and that is a heavy lift at Tier 1 scale.”
That governance hurdle matters. Network signals influence payment approvals. But liability stays with the institution. Shared data may inform scoring, but accountability doesn’t transfer, Mohapatra said.
There’s also an inherent confirmation lag in consortium models. Cross-bank validation takes time. That doesn’t mean decisions slow down. Network intelligence builds a shared history of risky entities in advance. When a real-time payment occurs, that history is already available in the risk score. The value lies in prior validation, not post-transaction review.
Systemic Risk and Differentiation
Shared intelligence introduces concentration risk. If many institutions rely on similar consortium signals, attackers may study one framework and apply that knowledge across others. Convergence around common data sources can create uniform exposure.
“The systemic risk question is legitimate: If large banks converge on the same consortium signals, a sophisticated threat actor only needs to learn one system. But the industry has navigated concentration risk before, and the answer has consistently been layering shared signals with proprietary detection logic,” Abadir said.
While layering mitigates uniform exposure, shared data becomes a foundation. This differentiation may shift from signal ownership to orchestration quality. How banks calibrate thresholds, manage friction and integrate signals into enterprise platforms will matter more than raw data exclusivity.
Under this model, governance is central. “Network intelligence does not change the decision or make a decision. So, liability for the decision remains with the bank,” Mohapatra said.
Other AI domains offer cautionary lessons. Research has shown that shared AI systems can introduce new attack surfaces if not carefully governed. Hidden instructions embedded in AI summarization tools have demonstrated how adversaries can influence automated outputs.
Network intelligence represents a meaningful shift in how fraud is analyzed. It changes the core analytic focus from anomalies to connections. It expands visibility beyond institutional walls and into the broader ecosystem where fraud networks operate.
But at the same time, it doesn’t displace existing AI foundations. Internal models remain central to decision-making. Governance, integration and risk controls remain complex and institution-specific.
“Consortium intelligence as a shared foundation with institution-specific model tuning on top is a hard target to beat,” Abadir said.