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Excellent · Verdica composite score based on three weighted sub-scores from public regulator data.
7 active sources · last successful refresh Aug 12, 2026
source data currentHow this is calculated
Composite = Registration & Compliance × 35% + Disciplinary History × 40% + Business Profile × 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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AI EARN LTD scores 789 — these providers rank higher on Verdica's composite trust score.
The Verdica score is a composite trust score from 300 to 850, computed from registration status, disciplinary history, and business risk indicators maintained by the SEC and FINRA. A score of 789 places AI EARN LTD in the strong (740–799) 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 SEC and FINRA. Each sub-score reflects a different dimension of financial services compliance — regulatory disclosures, disciplinary actions, and firm risk factors — and is normalized against industry peers before being aggregated to the 300–850 scale.
Verdica pulls public records from the SEC and FINRA: registration status, disciplinary history, and business risk indicators. 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 regulatory sanctions, customer arbitrations, or firm-level disclosures. 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.