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15/08/2026 6:11 pm
TL;DR: Fragmented point solutions leave banks blind to AI-driven fraud, according to Fingerprint, with 41% of fraud attempts in its survey described as AI-driven and financial institutions reporting 54% exposure. The governance lesson is that fraud, identity, and compliance now need shared, real-time signals rather than isolated checks.
NHIMG editorial — based on content published by Fingerprint: device intelligence for banking fraud, compliance, and identity signals
By the numbers:
- In Fingerprint's survey of over 300 business leaders, 41% of all fraud attempts were AI-driven, with financial institutions reporting 54% exposure.
- Only 27% of U.S. organisations said fraud prevention was jointly owned by finance and security teams.
Questions worth separating out
Q: How should banks use device intelligence without creating another silo?
A: Banks should treat device intelligence as a shared decision layer, not a stand-alone fraud tool.
Q: Why do AI-driven fraud attacks bypass traditional KYC controls?
A: Traditional KYC controls are designed to verify a person once, not to prove that the same person is still present later.
Q: What breaks when fraud and identity teams work in silos?
A: When fraud and identity teams work in silos, policy decisions diverge, exceptions are handled inconsistently, and no one owns the full trust lifecycle.
Practitioner guidance
- Unify fraud and identity review queues Route onboarding, authentication, and case-management events into a shared queue so fraud analysts and identity teams review the same device and session context before escalating.
- Add cross-session correlation to KYC workflows Preserve device and behavioural continuity across sessions so repeated fingerprints, geolocation shifts, and account reuse are visible during KYC and AML monitoring.
- Measure false-positive reduction by context reuse Track whether the same device intelligence signal can resolve multiple investigations, rather than generating isolated alerts that each team must re-interpret.
What's in the full article
Fingerprint's full article covers the operational detail this post intentionally leaves for the source:
- How device intelligence evaluates hundreds of browser, network, and device signals in practice
- Examples of how banks can use shared intelligence to cut false positives without weakening customer verification
- The specific ways device continuity supports fraud, compliance, and identity workflows across sessions
- Why AI-driven fraud changes the balance between friction, onboarding speed, and auditability
👉 Read Fingerprint's analysis of device intelligence for banking fraud and compliance →
Device intelligence in banking: what it means for fraud and IAM teams?
Explore further
15/08/2026 6:18 pm
Fragmented fraud governance is now a trust failure, not just an efficiency problem. When fraud, identity, and compliance teams rely on separate telemetry, they create inconsistent risk decisions for the same customer journey. That inconsistency is exactly what adaptive fraud exploits because the attacker only needs one disconnected control path to succeed. For practitioners, the governance test is whether one decision fabric can support KYC, AML, and fraud review together.
A question worth separating out:
Q: Who is accountable when fraud controls block legitimate customers in real time?
A: Accountability should sit with the team that owns the end-to-end decision path, not only the fraud model. If checkout, identity, and risk signals are not orchestrated into one control, then the business is responsible for the conversion loss as well as the fraud loss. Governance needs shared ownership across fraud, product, and security leaders.
👉 Read our full editorial: Device intelligence closes the fraud and compliance blind spot in banking
