By: Vivienne Ashford
Financial institutions and fintech companies are navigating a tighter regulatory climate for both anti-fraud controls and data privacy. Across the United States, more than 40 states introduced or considered hundreds of consumer privacy bills in 2024, many of which specifically address biometric identifiers and how long they can be retained. Illinois amended its Biometric Information Privacy Act to narrow how damages are calculated, while Colorado expanded its privacy law to place new limits on biometric data use, signaling that scrutiny of facial and fingerprint data is intensifying.
At the same time, financial crime regulations are pushing institutions to strengthen Know Your Customer and Anti-Money Laundering controls. Guidance for fintech companies increasingly promotes artificial intelligence, digital identity verification, and biometrics for onboarding and monitoring, while warning that noncompliance can result in significant financial penalties. Industry reports describe biometric KYC as a growing standard, combining document checks with facial recognition and liveness detection so institutions can confirm that the person presenting an ID is the same person on record.
This regulatory mix creates a tension: firms are encouraged to adopt face-based verification to reduce fraud and meet compliance requirements, but must do so under privacy rules that restrict how biometric data is collected, processed, and shared. For companies operating across multiple jurisdictions, aligning those expectations has become a central design challenge.
How CypherFace Encrypts Faces for Compliance and Privacy
CypherFace, a U.S.-based fintech founded in 2024, has built its payment verification system around encrypted facial biometrics designed to support both compliance and privacy demands. Its platform combines facial recognition with AI-driven liveness detection so users are verified in real time before a payment or account action proceeds. The company offers versions for both transaction-level checks and onboarding, where biometric verification supplements traditional document-based KYC.
Each facial scan is encrypted at capture and converted into a non-reversible biometric template, according to CypherFace. Rather than storing images or video, the system generates a mathematical representation of the face. These templates remain internal, while client businesses receive only a pass-or-fail result. CypherFace says this approach limits the exposure of sensitive biometric data while still supporting regulatory obligations.
Because templates cannot be reverse-engineered into facial photos, they are less sensitive than raw images in the event of a breach, though still treated as protected biometric data under many state laws. That distinction matters as regulators, including the Federal Trade Commission, signal that misuse of facial recognition and weak safeguards will remain enforcement priorities.
Speed vs. Safeguards in Real-Time Verification
Speed of verification is another pressure point in digital payments and onboarding. Financial services companies are adopting real-time identity checks that combine document recognition with facial biometrics and liveness detection, allowing identity confirmation in seconds rather than through manual review. Advocates argue this improves user experience while strengthening fraud prevention, provided data retention and consent are clearly managed.
CypherFace integrates with existing payment infrastructure through an API, enabling merchants and processors to trigger facial checks without major system changes. In public case examples, the company reports that real-time biometric checks helped flag more than 1,200 suspicious transactions in 45 days and reduced chargebacks by 62 percent in one segment.
Whether encrypted facial biometrics can consistently balance regulatory compliance, privacy safeguards, and the demand for near-instant verification remains an open question for the industry. As biometric rules continue to evolve, CypherFace illustrates how one fintech is attempting to navigate that complex space — using advanced facial recognition and liveness detection to meet KYC and AML expectations and standards, encrypting biometric templates to significantly reduce privacy risk, and embedding these checks closely enough to transactions to keep user friction low while enhancing overall security.
