H4WIN (SCAM 31K)

We begin with a structured operator dossier that consolidates license provenance, ownership disclosures, financial solvency indicators, and regulatory dispositions. This baseline is mapped to our internal risk taxonomy (licensing robustness, fund segregation, RTP governance, AML/KYC posture, dispute recourse). We then normalize the inputs against a cross-operator prior to detect outliers and hidden tail risks.

We request auditable artifacts: RNG certifications (scope + renewal cadence), payout logs (anonymized), reconciliation policies, cash-out SLAs, and incident registers for withheld payments. Our reviewers verify that certification scope maps to actual game inventory, and we cross-reference payout behavior with community-sourced claims to detect systematic friction.

We maintain a benchmark corpus for payment rails latencies, dispute resolution timelines, bonus terms entropy, and risk flags. H4WIN (SCAM 31K) is positioned in a quartile model against similar-license cohorts. Any deviation outside interquartile ranges triggers manual inspection and a variance memo that is reviewed before publication.

We separate evidence collection from scoring. Collection is checklist-driven and source-attributed. Scoring uses a rubric with locked weights, versioned over time. All anomalies require a two-reviewer concurrence and a third-eye approval for critical findings, ensuring epistemic humility and consistency.