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The 4.18% figure underscores that fraud remains a significant threat, requiring continuous improvement in document verification and liveness detection to stay ahead of sophisticated attackers. Thus, a robust biometric self-exclusion system must integrate fraud detection at the identity proofing stage, ensuring that the biometric data linked to an exclusion order belongs to the correct individual. This fraud includes synthetic identities (completely fabricated credentials) and stolen documentation used to bypass exclusion lists. In 2025, digital identity verification checks flagged 4.18% of attempts as fraudulent, according to SQM Magazine (March 2026). The bias challenge is not merely technical; it also involves ensuring that the training data for facial recognition algorithms represents all population groups to avoid disparate impact. Regulators like OLG are already mandating specific metrics, while lobby groups and fraud statistics highlight the risks that must be managed.
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