Excellent · Verdica composite score based on three weighted sub-scores from public regulator data.
5 active sources · last successful refresh Oct 14, 2026
source data currentHow this is calculated
Composite = License Standing × 35% + CFPB Complaint Load × 40% + Response Quality × 25%, scaled to 300–850.Read full methodology →
Each sub-score starts at a baseline and adjusts based on real regulatory-record signals. Zero-delta rows show what we checked and confirmed clean. “Data quality” indicates how much source data informed the score.
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CANO, ELI scores 836 — these providers rank higher on Verdica's composite trust score.
The Verdica score is a composite trust score from 300 to 850, computed from consumer complaint history and debt-collection licensing maintained by the CFPB and state licensing boards. A score of 836 places CANO, ELI in TX in the excellent (800–850) band. The score reflects publicly available regulator data — it is not a rating from Verdica itself and does not incorporate paid reviews or advertising.
Verdica combines three weighted sub-scores drawn from the CFPB and state licensing boards. Each sub-score reflects a different dimension of collection compliance — CFPB complaint volume, resolution outcomes, and state license status — and is normalized against industry peers before being aggregated to the 300–850 scale.
Verdica pulls public records from the CFPB and state licensing boards: consumer complaint history and debt-collection licensing. Data is refreshed on rolling cycles, typically weekly to monthly depending on the source agency's own update schedule. See the "source data current" indicator on the entity page for the last successful refresh.
Common negative signals include high complaint volume, unresolved disputes, or license suspensions. Any of these appear in the underlying regulator records and pull the sub-score down; the composite reflects the weighted impact.
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Yes. Entities can file a dispute directly from the score page, and business owners can claim their profile. Verdica reviews disputes against the underlying regulator record; the score is updated when the source data is corrected. Scores are recomputed automatically as new regulator data arrives.