How Device Intelligence Helps Banks Assess Fraud Risk Beyond Fingerprinting

Summary

ThreatFabric explains how device intelligence combines device, location, call, malware and remote-access signals to assess digital banking fraud risk beyond device fingerprinting, and how banks can use those signals in transaction monitoring.

Key points

• Device intelligence analyzes device attributes and security posture to estimate whether a digital banking session is fraudulent, rather than relying on identification alone.
• The article recommends combining static and dynamic device attributes, including installed apps and paired devices, to create more persistent fingerprints.
• Location signals can include prior user activity, travel plausibility and the reputation of an IP address, ISP or ASN.
• Call type and duration may help assess vishing risk; ThreatFabric says only about 1% of genuine sessions in its and industry data involve a live call.
• ThreatFabric reports a 94% increase in mobile malware families targeting financial apps over three years and describes using threat intelligence and heuristic rules for detection.
• The article identifies active remote access tools, malware, rooted or jailbroken devices, emulators and code injection as risk signals.
• Banks can feed device risk signals into transaction-monitoring systems in real time and combine them with customer and transaction context.

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