Behavioral scoring at onboarding — even with no history.
Each one gets past a check run on its own. Pick a case and see where Zarv ID stops it.
Illustrative simulation. Values are not from a real customer.
Fraud at contracting costs more than preventing it. Zarv ID blocks before any contractual bond is established.
Document verification (ID, driver's license, passport), facial biometry with liveness detection, deepfake and tampered document detection. Synthetic identities blocked. PEP screening and Zarv Restricted Profiles check.
Sophisticated fraudsters pass individual KYC. What gives them away is the network around them. The graph detects what documents don't show.
Every individual is analyzed in the context of their network. Detects fraud clusters: if someone is linked to 3+ flagged identities, their score rises. Identifies straw buyers and fraudsters invisible to bureaus.
Profiles without history get rejected or overcharged. With Zarv ID, you can price what was previously unknown risk — more volume, same loss ratio.
Score calibrated for insurance and vehicle credit. Works for people with no prior policy or credit history. Detects geolocation shifts, atypical patterns, and incoherent history. Output: numeric score + risk band + flags.
Underwriters don't need another score to interpret. They need a decision. Zarv ID delivers the decision — with data to back it.
Not just a score — a premium band recommendation. Segmentation calibrated to your portfolio. API integration with your pricing engine. Compatible with existing actuarial models.
Zarv ID builds a relationship graph integrating these data sources:
Low bureau score due to lack of history → rejection or excessive premium → client leaves for competitor.
→Behavioral graph reveals the driver lives in a low-risk area, works regularly, and has no links to suspicious profiles → competitive premium band → client accepts → policy issued with accurate pricing.
Automatic renewal — no flags raised.
→Relational risk flag: the holder's SSN is linked to 4 claims at other insurers in 18 months → underwriter reviews → fraud pattern identified → renewal denied.
Rejected due to unknown risk.
→Graph shows consistent income patterns, stable employment location, active business links → positive risk scoring → financing approved with adequate guarantees → client stays current.
Multiple applications processed without red flags.
→Dealership EIN appears in the graph linked to other flagged EINs — fraud cluster pattern → automatic alert before credit release.
Flat premium for entire fleet, regardless of individual driver risk.
→Each driver scored individually → fleet segmented into risk bands → insurer issues differentiated policy → company reduces total premium.
Leasing + Self-InsuranceStart with no code and integrate when it makes sense.
A branded signup page, published by link, on your site or on WhatsApp.
REST with JSON responses and webhook alerts — integrated in minutes with our ready-made prompts for Claude Code, Codex and the like.
API DocumentationScore, risk band and the reason behind every flag for your team.
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