As a result, many businesses respond by adding more checks: document uploads, selfies, liveness prompts, one-time codes, and device challenges. Yet, fraud losses continue to rise, particularly from synthetic identities and deepfake-enabled attacks, even as user abandonment increases. Human intuition is no safeguard either — recent research shows that only a tiny fraction of people can reliably spot high‑quality deepfakes, and biometric liveness detection engines are equally fallible when they only see the surface. When a single datapoint can no longer anchor trust, identity can’t be treated as a binary yes/no outcome. It has to be viewed as a constellation of signals and behaviours that evolve.
By contrast, real users leave messy, layered histories. Their email addresses often tie back to years of activity, including exposure in data breaches; their devices reappear across sessions; their IPs and locations follow plausible routines. Contextual fraud and digital footprint analysis move the focus from “Does this face match this document?” to “Does this identity behave like a real person with a real past?” In other words, the question shifts from proof of face to proof of existence.
Why Traditional IDV Punishes the Wrong People
Meanwhile, sophisticated attackers optimise for these exact constraints. Using deepfake engines, synthetic overlays, or replayed video, they can generate “ideal” biometric samples that are explicitly designed to satisfy automated checks on the first attempt. The result is a perverse inversion: genuine customers encounter friction and failure because reality is messy, while synthetic identities sail through because they were engineered to look perfect to a machine. Systems that hinge on one inflexible moment of verification end up punishing error instead of detecting intent.
In practice, conditional approvals allow low-risk users to proceed with minimal friction, supported by strong, low-touch signals, such as digital footprint strength, device reputation and consistent behavioural patterns. Higher‑risk users are not blocked by default but are subject to stepped‑up checks when their actions justify it. When funds move, limits increase, sensitive data changes, or behaviour diverges sharply from their baseline. Identity stops being a one‑time gate and becomes an ongoing negotiation between trust, risk and user intent.
Fraud-Native IDV and the Always-On Journey
Modern platforms now capture and interpret real‑time signals across devices, networks and digital footprints, turning raw telemetry into risk scores that evolve with each session. In a fraud-native IDV flow, every subsequent touchpoint — such as login, device change, or high-value transaction — becomes another opportunity to validate that the person behind the account is still the same, still legitimate and still behaving like a human rather than a script or a coordinated fraud cell. Liveness biometric checks still play a role, but they are integrated as part of this larger context, rather than acting as a single point of failure.
Behavioural and Device Intelligence as Counter‑Biometrics
Toward Adaptive, Multi‑Layer Identity
