Methodology
How AutoRiskIQ scores risk pressure
AutoRiskIQ translates public, regulator-grade data into transparent, location-level risk pressure scores. The goal is to explain why outcomes vary by location, not to recommend insurance products.
What the scores represent
- Scores are comparative and percentile-based, not absolute.
- Signals are location-level and aggregated; no personal data is used.
- Scores describe relative risk pressure, not individual outcomes.
- AutoRiskIQ does not recommend carriers or provide financial advice.
Normalization highlights
- Percentile scoring keeps locations comparable across sizes.
- Normalization avoids raw-count bias for large states or counties.
- Trends highlight direction, not promises of outcomes.
Scoring flow
From public data to transparent scores
V1 scoring process
Collect public, regulator-grade inputs
Each pillar uses public datasets with defensible, auditable sources.
Normalize by exposure and scale
Raw counts are adjusted for population, market size, and exposure.
Score each pillar independently
Pillars are scored on a common percentile scale with trend context.
Blend into a composite score
Core and supporting pillars are weighted into a composite score.
Pillars
Core and supporting risk signals
Weights shown for composite scoring
Accident & Exposure Risk
Core risk
Likelihood of crashes and exposure pressure driven by crash frequency, severity, and traffic density.
Weather & Environmental Risk
Core risk
Catastrophic loss pressure driven by severe weather exposure and seasonal volatility.
Theft & Fraud Exposure
Supporting risk
Comprehensive claim exposure driven by vehicle theft and fraud environment signals.
Cost Pressure & Repair Economics
Core risk
Repair severity and cost escalation driven by labor, parts, and vehicle mix.
Claim Friction & Legal Context
Core risk
Relative claim friction pressure driven by complaint volume, regulatory posture, and legal environment signals.
Market Structure Context
Context risk
Market structure context that influences pricing power and carrier behavior.
What we exclude
- No scraped reviews or social media sentiment
- No anecdotal complaints or unverifiable denial rates
- No leaked or proprietary datasets
Data landscape
Each pillar has a documented data landscape with citations and coverage notes. Use the data overview to see source coverage and updates.