Explainer
How AutoRiskIQ normalizes public data for fair comparisons
AutoRiskIQ translates public datasets into comparable, location-level signals. Normalization ensures that large states do not overwhelm small ones, and that signals reflect relative exposure instead of raw counts.
Why normalization matters
Raw counts can mislead comparisons across locations with different populations and exposure levels. Normalization helps ensure that scores reflect relative risk pressure instead of absolute scale.
How to interpret the scores
- Scores are comparative and describe relative risk pressure.
- They are location-level signals, not individual driver risk.
- They do not recommend carriers or promise savings.
Normalization steps
How we keep scores comparable
Public signal overview
Use public, regulator-grade sources
Inputs come from authoritative datasets that can be cited and audited.
Normalize for population and exposure
Signals are adjusted to reflect relative exposure, not raw counts.
Compare within consistent baselines
Scores reflect percentile-based comparisons across locations.
Separate pillars, then blend
Each risk pillar is scored independently before creating a composite.
Sources and methodology
AutoRiskIQ documents data sources and scoring approach in the data landscape docs and methodology overview. These sources are public, regulator-grade, and cited in the pillar documentation.