Topic starter
06/11/2026 6:39 pm
TL;DR: AI is making fraud easier to scale, with synthetic identities, voice deepfakes and agentic automation bypassing KYC and behavioural checks while Deloitte projects $40 billion in AI-enabled fraud losses by 2027. Zumigo’s analysis argues that mobile history and carrier signals are now central because documents, biometrics and behavioural evidence can all be forged or trained away.
Editorial analysis by NHI Mgmt Group, based on content published by Zumigo: “Why Real-Time Identity Intelligence Matters”.
By the numbers:
- Agentic fraud attempts grew 450% in 2025, according to LexisNexis Cybercrime Report research cited by Zumigo.
- Login attack rates increased 89% in 2025, according to LexisNexis Cybercrime Report research cited by Zumigo.
Key questions
Q: How can security teams detect synthetic identity fraud?
A: Look for identities built from mixed or inconsistent attributes, such as thin files, repeated contact details, and mismatched device or location signals.
Q: Why do SIM swapping attacks remain so effective against OTP-based authentication?
A: SIM swapping works because the phone number becomes the attacker’s delivery channel for one-time passcodes.
Q: How should teams use mobile signals in account recovery?
A: Use mobile signals only as one factor in a step-up recovery flow, not as sole proof of identity.
Practitioner guidance
- Promote carrier signals into core identity proofing Use SIM swap recency, forwarding status, number tenure and port history as first-class signals in onboarding, login and recovery decisions.
- Add network-aware checks before issuing OTP-based step-up Block or challenge authentication flows when the phone number shows recent porting, disposable VoIP characteristics or active call forwarding.
- Tune fraud models for velocity and repetition anomalies Look for bursty application volume, unusual query frequency, repeated number patterns and inconsistent identity reuse across institutions.
Bottom line: AI-enabled fraud now exploits the gap between what can be verified visually and what can be verified from durable network history.
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06/11/2026 6:41 pm
Mobile trust has become an identity control plane, not a contact-detail layer. Fraud teams have treated phone numbers as metadata for too long, but the evidence in this topic shows that number tenure, port history and forwarding status are now part of the trust decision itself. When documents, biometrics and behavioural signals can all be manipulated, the carrier record becomes one of the few durable indicators left. Practitioners should therefore stop treating telecom signals as optional enrichment and start treating them as core proofing inputs.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the best way to spot AI-driven fraud at scale?
A: The best way is to combine automated detection with layered controls that score device, behaviour, network, transaction and account signals in real time. Use rules for known fraud patterns, anomaly models for scale, step-up verification for risky events, and case management for analyst review so false positives and adaptive attacks are handled consistently.
👉 Read our full editorial: Real-time identity intelligence is closing the AI fraud gap