Zarv
Zarv Signal

Risk moves. So does pricing.

Continuous monitoring of behavior, location, and exposure. Pricing that tracks reality.

The problem

Risk changes after the policy is written.

The price was set on day 0. Drag the timeline and see what happens to the risk over the next 90 days.

Risk scoreDay 0 / 90
02550751000306090

Without Signal: The insurer only finds out at renewal — or at the claim.

Signal alerts

Everything within the pattern so far.

Illustrative simulation. Values are not from a real customer.

34x
Faster mitigation
$6M
Theft prevention saves
93%
Vehicle recovery
99.9%
Uptime SLA

Continuous intelligence. Not periodic check-ins.

GPS + LPR + mobile data, real time.

Risk that changes after the policy

Common tools deliver tracking — position and speed. Signal delivers intelligence: what those data points mean for this contract's risk.

Continuous feed of GPS, LPR/OCR cameras, and mobile device data. Full trajectory history with speeds, schedules, and zones. Real-time dashboard or API. Weekly/monthly behavior reports.

Continuous monitoringlive
82 km/h
GPSLPRMobile
Adaptive per individual, not static per region.

Off-pattern, not off-region

Static geofences generate false positives. Signal solves this with per-individual geofences — not per-region.

Geofences created based on each driver/vehicle profile. Alerts when the vehicle deviates from expected patterns. Distinguishes behavior change from one-time events (business trip vs. systematic deviation).

Dynamic geofence
Off expected pattern
Alert before the incident.

Theft spotted before it's reported

Prevention is cheaper than recovery. 34x faster when the alert arrives before the incident.

Models trained by usage type (personal insurance, corporate fleet, financed). Alerts when usage deviates from baseline. Classification: observe / investigate / act. Examples: night routes in high-risk zones, abnormally slow speed (possible tow), vehicle at theft-associated locations.

Anomaly detection
observeinvestigateact
Alerts with context, not noise.

Alerts that say what to do

An alert without context is noise. Signal delivers alerts with behavioral context — the operator knows why the vehicle was flagged and what to do.

Push via API, webhook, or dashboard. Configurable thresholds by event and severity. Integration with SIEM, CRM, and claims workflow. Automatic escalation when anomaly persists.

Predictive alerts
Night route · high-risk zone
Abnormally low speed (tow?)
Theft-associated location
Reprice based on real behavior.

Premiums that follow behavior

Low-risk drivers get reduced premiums, increasing retention. Drivers who worsened get repriced before they file a claim.

UBI isn't just insurance by mile. It's repricing based on real risk. Monthly score updates. Output: adjustment recommendation per contract with justification. Compatible with renewal or mid-term adjustment. API for policy system integration.

UBI · repricing
−12% low risk+18% worsened
Monitor the asset, not just the borrower.

Default seen before the first missed payment

Lenders monitor the borrower, not the asset. Signal monitors the asset — and detects when behavior suggests the contract is at risk.

Monitors financed vehicle behavior against expected patterns. Flags default intent: atypical usage, unreported transfer of possession, vehicle in area different from contract. Alert to collections before the first missed payment.

Early default detection
expectedactual
Default intent flagged

In practice.

Auto Insurance

Mid-term repricing — driver switches to delivery app

Insurer discovers at renewal (12 months). Already paid claims for a risk it didn't price.

Anomaly detected in month 3. Alert to risk team. Options: contact client, issue coverage extension, adjust premium mid-term.

Credit & Lending

Collateral monitoring — vehicle being prepped for irregular sale

Default arrives as a surprise. Asset already moved.

Suspicious behavior flag: vehicle in different neighborhood, parked at atypical locations → alert to preventive collections → contact before missed payment → refinancing or recovery.

Fleet

Operational anomaly — corporate vehicle for personal use

Discovered during maintenance audit. Odometer doesn't match reported usage.

Personal use anomaly flagged automatically → report generated → fleet manager enforces internal policy.

Proven results.

34x
faster mitigation
Predictive alert vs. reactive response
$6M
in theft prevention saves
TagPro — 2024
93%
vehicle recovery rate
vs. 45% market average
Loss ratio
Fewer incidents = lower loss ratio
Customer stories

Proven at scale.

TagProInsurer
Auto Insurance (B2C)
Zarv IDZarv SignalZarv Lens
$6M
in theft prevention saves in 2024
85–93%
vehicle recovery rate
+3pp
gross margin increase (YoY)
How it worked
  1. 1New clients onboarded via Zarv ID (KYC + scoring)
  2. 2Portfolio monitored in real time via Zarv Signal
  3. 3Losses investigated via Zarv Lens with evidence generation
Arval / BNP ParibasLeasing + Self-Insurance
Corporate Fleet (B2B)
Zarv SignalZarv Lens
85%
vehicle recovery rate
18%
reduction in fraud premiums
Auto
automated claims management
How it worked
  1. 1Fleet monitored continuously via Zarv Signal
  2. 2Claims reconstructed automatically via Zarv Lens
  3. 3Zarv ID integration underway for new lessee onboarding
Getting started

Monitoring on day 1. Integrated in minutes.

Use the telematics you already have and start from the dashboard.

Day 1

Operations dashboard

Alerts, routes and a score per vehicle, ready for your team — white-label if you want it.

Minutes

API and webhooks

Real-time alerts in your policy, collections or claims system — in minutes with our ready-made prompts.

No new hardware

The telematics you already have

Existing OBD and embedded GPS, LPR cameras, and a mobile SDK for your customer's app.

Per asset
monthly price per vehicle
Mobile SDK
for your customer's app
White-label
dashboard with your brand
Security and compliance
See every control

See risk before it costs you.

GDPR & CCPA Compliant · No commitment · Live in minutes