Kreate Face : AI-Powered Biometric Identity Verification for Digital Banking

About the Project

Sector: Digital-First Private Banking | Platform: KreateFace | Regulatory Framework: RBI V-CIP | PMLA

A digital-first private sector bank had built something genuinely impressive: a growing customer base, a mobile-first experience, and a customer segment that had never visited a branch and never intended to.

What it had not solved was identity. The onboarding process required customers to schedule a video call with a live agent during business hours , a process that introduced wait times, scheduling friction, and a material drop-off rate at the exact moment of highest intent. For existing customers, a layered combination of passwords, PINs, and OTPs generated enough friction that authentication failures were the single largest driver of digital support contacts.

And despite all of that friction , all of it imposed on genuine customers , identity fraud continued. Credentials were stolen through phishing and SIM swap. Synthetic identities slipped through document-only checks. The bank was losing customers at the door and failing to stop the ones it needed to stop.

Kreate Technologies was brought in to rebuild identity verification around a principle that should have been obvious from the start: the one credential that cannot be forged, shared, stolen, or forgotten is the person themselves.

The Challenge

Problem Statement: The bank's identity verification model was simultaneously too hard for genuine customers and not hard enough for fraudsters. Friction and fraud were coexisting, which meant the friction was not buying the protection it was supposed to.

Challenges

V-CIP onboarding constrained to business hours and live agent scheduling , introducing multiple abandonment points at the moment of highest customer intent
Password and OTP failure contacts exceeded the fraud savings those authentication layers were meant to provide
Account takeover fraud via phishing, SIM swap, and credential stuffing was passing OTP-based authentication without reliable detection
Synthetic identity fraud using forged or misappropriated documents bypassed document-only verification with growing frequency
No continuous post-login identity assurance , an authenticated session could be operated by an impostor without triggering any detection signal

Our Solution

Kreate Technologies deployed KreateFace as a multi-layer biometric intelligence platform , not a single biometric feature, but a coordinated defence across the full identity lifecycle: onboarding, ongoing authentication, and in-session fraud detection. Every capability was designed around the same principle: effortless for the real person, reliably impossible for everyone else.

Implementation Phases

Phase 1 : Months 1–2

Mobile app integration; facial recognition model calibrated on bank's customer demographic profile; biometric data governance framework approved by legal and compliance

Phase 2 : Months 3–4

Biometric enrolment offered as opt-in to existing customers; facial auth available as supplementary option; liveness detection validated against bank-specific attack testing

Phase 3 : Months 5–7

Automated V-CIP deployed for new customer onboarding; behavioural biometrics engine activated in learning mode; real-time drop-off monitoring with weekly calibration

Phase 4 : Months 8–10

Behavioural trust scoring activated for customers with established profiles; voice biometrics integrated with IVR and call centre; fraud intelligence engine connected

Phase 5 : Months 11–14

Biometric authentication as default for enrolled customers; full fraud intelligence monitoring active; regulatory reporting module configured; performance audit completed

Results

Business Outcomes

100% — Any-hour onboarding , V-CIP available 24/7 with no agent required
68% — Of fraud detected in-session via behavioural biometrics
35% — Reduction in authentication-related support contacts
<2 min — V-CIP completion time , from multi-day agent scheduling

Operational Impact

Onboarding drop-off at the V-CIP step fell materially , customers who began applications outside business hours could complete them in the same session, without scheduling a call they would not keep
Behavioural biometrics added a detection capability that had not previously existed: identifying sessions where the behavioural pattern indicated an impostor had taken over after initial authentication , catching fraud that credential-based systems are architecturally incapable of detecting
OTP and password failure contacts fell significantly as reliable biometric authentication replaced the failure-prone credential fl ow for enrolled customers
Identity fraud detection rate improved materially over document-only checks , with the improvement concentrated precisely in the synthetic identity and account takeover patterns that had been slipping through
The bank's legal and compliance teams approved deployment with confidence , because the privacy architecture was built before any customer was exposed to the platform, not retrofitted after concerns were raised

Key Takeaways

Demographic consistency is a technical requirement, not a policy position. Any biometric system deployed at scale must be equally accurate across all customer subgroups. Disparities are defects. Monitoring must continue after deployment, with automatic alerts when accuracy diverges.

Friction and fraud are not a trade-off . A biometric process that genuine customers find effortless is also harder to attack , because the signals from real customers are behaviourally coherent in ways impostors cannot replicate.

Privacy architecture enables deployment. One-way template transformation, HSM key management, and explicit consent flows are not compliance checkboxes. They are what gave the legal and compliance teams the confidence to approve go-live.

Adoption must be earned, not mandated. Deploying biometric authentication as an opt-in experience , and letting the quality of the experience drive adoption , produces more durable enrolment rates than forcing the transition.

Kreate Face : AI-Powered Biometric Identity Verification for Digital Banking

About the Project

The Challenge

Challenges

Our Solution

Our Solution

AI Facial Recognition:

Liveness Detection:

Automated V-CIP:

Behavioural Biometrics:

Voice Biometrics:

Privacy-First Architecture:

Implementation Phases

Phase 1 : Months 1–2

Phase 2 : Months 3–4

Phase 3 : Months 5–7

Phase 4 : Months 8–10

Phase 5 : Months 11–14

Results

Business Outcomes

Operational Impact

Key Takeaways