Notifications
Clear all
Topic starter
11/07/2026 8:22 pm
TL;DR: Mandatory reimbursement rules for authorised push payment fraud are shifting loss responsibility to banks, while AI-driven deepfakes and injection attacks are helping criminals bypass legacy onboarding checks, according to Oz Forensics. The control problem is no longer just verifying a face, but proving the signal is live and trustworthy under reimbursement pressure.
NHIMG editorial — based on content published by Oz Forensics: Stop Reimbursement Costs: The Biometric Defense
By the numbers:
- UK banks reimbursed £112 million to victims, with a reimbursement rate hitting 88%.
- The article says Oz Forensics has achieved 100% detection accuracy in labs such as BixeLab.
Questions worth separating out
Q: How should banks stop biometric fraud when deepfakes and injection attacks are in play?
A: Banks should focus on capture integrity, not just face recognition accuracy.
Q: Why do APP fraud reimbursement rules change identity control priorities?
A: They convert identity failure into a direct financial liability.
Q: What breaks when biometric liveness is treated as a user-experience feature only?
A: Teams underinvest in assurance testing and overestimate face matching.
Practitioner guidance
- Map every identity decision to a reimbursement risk path Trace where onboarding, liveness, step-up authentication, and payment approval can each trigger a reimbursable loss.
- Test for injected and replayed capture streams Include emulator, virtual camera, replay, and synthetic video tests in biometric assurance.
- Separate real-user friction from attacker friction Use passive liveness and equivalent low-friction controls to keep conversion high while raising the attacker’s cost.
What's in the full article
Oz Forensics' full article covers the operational detail this post intentionally leaves for the source:
- The full regulatory context behind mandatory reimbursement rules across the UK, EU, Singapore, Australia, and Brazil.
- The specific biometric and liveness techniques the vendor describes for reducing false acceptance during onboarding.
- The Injection Attack Detection criteria and lab-testing references used to support the product claims.
- The article's discussion of conversion, friction, and deployment trade-offs for financial institutions.
👉 Read Oz Forensics' analysis of biometric controls and APP fraud liability →
APP fraud liability shifts and biometric controls: what banks need?
Explore further
11/07/2026 8:52 pm
Fake-signal trust is the real governance gap: biometric programmes often assume the capture channel is trustworthy once a face match succeeds. That assumption fails when the actor can inject a synthetic or replayed signal, because identity proof becomes detached from physical presence. The implication is that banking identity assurance must treat capture integrity as a first-class control boundary.
A few things that frame the scale:
- 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, according to Ultimate Guide to NHIs.
- Only 5.7% of organisations have full visibility into their service accounts, which means identity teams often cannot see the full population they are expected to govern.
A question worth separating out:
Q: Who is accountable when synthetic identity fraud passes onboarding controls?
A: Accountability sits with the institution that accepted the trust decision, especially where reimbursement rules place losses back on the bank. IAM, fraud, and payments teams should share ownership of the capture-path control model before a loss occurs.
👉 Read our full editorial: Biometric defense against app fraud liability shift in banking
