How AI defines provider integrity

What is "honesty" in practical terms

1. Mathematical correctness: actual RTP in the statistical corridor; stable paytables; lack of personal "twisting" of chances.

2. Generation of outcomes: certified RNG/VRF, commit-reveal/sidelogs; reproducibility of tests.

3. Transparency of conditions: clear rules of bonuses/tournaments/promo; public version changes.

4. Responsible play (RG): predictive risk signals and correct interventions; no pressure/manipulation.

5. Payment discipline: predictable ETA, lack of "hidden filters" for output, correct KYC practice.

6. Security and privacy: PII minimization, access logs, encryption, TTL compliance.

7. Operational reliability: uptime, delays, fault tolerance, documented degradation.

AI Audit Data Sources

Game telemetry: round/session events, wins/bets, RNG-sid/proofs.

Payment logs: deposits/conclusions, KYC statuses, holds, chargeback/returns.

RG/support: risk triggers, interventions, SLA responses, complaints.

Public artifacts: RNG certificates, build version, changelog paytables, RTP pages.

External signals: reports of auditors, user complaints (structured), status of sanctions/jurisdictions.

Infrastructure: uptime metrics, p95 delays, error logs.

💡 All raw data - with idempotency, exact timestamps and pseudonymization. PII - only when necessary.

Trust Score Model

AI collects metrics into a multilevel score: baseline score + thematic sub-scores + explanations.

Trust Score Layers

Math Integrity (30-40%): discrepancy between the actual RTP vs declared (by time windows and cohorts), stability of paytables, no individual deviations.

Outcome Verifiability (10-20%): presence of VRF/commit-reveal, reproducibility of tests, frequency of "check the round."

Promo Fairness (10-15%): share of transparent conditions, incrementality of promo, lack of "surprise rules."

RG Discipline (10-20%): the share of players with limits, the effectiveness of interventions, the neutral tone of communications.

Payments Reliability (10-15%): smart-ETA accuracy, share of delays outside SLA, KYC consistency.

Privacy/Security (5-10%): access incidents, TTL compliance, encryption/logs.

Operations (5-10%): uptime, p95 "stavka→podtverzhdeniye," documented degradation.

Each metric is accompanied by an explainability card: "what is measured," "threshold/norm," "actual value," "example of logs/proofs."

Algorithms and checks

1) RTP audit with variance

Bayesian estimate of window/game/bet variances; alerts at systematic bias.

Cohort control: device/client version/geo to eliminate "hidden" personalization.

2) RNG/VRF verification

Checking the chain of sides/signatures, commit-reveal match, periodic "black boxes" (mystery-tests).

Distribution anomalies (Dieharder-like tests) on flows.

3) Anti-manipulation promo

Causal models uplift (T-/X-learner) + bandits: we are looking for "overkill" with bonuses without increment, hidden conditions, aggressive texts.

Logs "why showed offer," frequency limits, failure with active RG restrictions.

4) RG observer

Risk models (L/M/H), intervention ladder, tone analysis.

False striggers/under-detection - in separate KPIs, manual check of "red" ones.

5) Payment and KYC patterns

Graph/behavioral models for fraud/AML, ETA monitor, proportion of holds outside politics.

Consistency of requirements: no "dynamic" barriers to withdrawal.

6) Operations and Security

Delay drift detector, crash-free, RBAC logs, DLP alerts on PII.

Solution Architecture

1. Event bus: standardized topics (rounds, payouts, promo, RG, AML), deduplication, PII protection.

2. Proof Storage: Signatures, Sid-Logs, (Opts.) anchoring hashes in the L2/L1.

3. Feature Store: aggregates by provider/game/jurisdiction/time.

4. Trust Engine: model ensembles + rules; calculates scores and explanations.

5. Policy Guardrails: "AI ≠ chances," prohibitions on personal changes to RTP/paytables, message control.

6. Shop windows:

Operator/regulator: dashboards, offloads, "why" for each flag.
Player: Short honesty card (RTP/profs/payouts/RG).

Protection against "gaming" rating

Random audit: hidden "auditors" and mystery pools.

Cross-validation of sources: comparison of operator and client telemetry, public and private logs.

Time aggregation: points do not "take off" instantly; need a steady period.

Penalties for retro edits: any retroactive changes are a separate flag.

Visibility threshold: significant rating changes - only after manual verification.

Explainability (XAI) and transparency

Cards: metric → rate → fact → logs/proofs → measurement date → model version.

History of changes: who and when updated the paytable/rules/tone of mailings.

For controversial cases - "man-in-the-circuit," SLA answers, appeal log.

Compliance and red lines

No personal modification of RTP/probabilities/near-miss for the player/segment.

Promo without hidden conditions and without pressure; RG restrictions are higher than marketing.

Sensitive signs (race, religion, etc.) are never included in features.

Signatures/hashes of logs, versioning of models and builds - by default.

Success Metrics (System KPI)

Accuracy of flags: precision/recall for confirmed violations.

RTP monitor stability: proportion of games in the corridor, response time to deviation.

RG/AML efficiency: proportion of correct interventions, time to fraud blocking.

Payouts: smart-ETA accuracy, proportion of delays outside SLAs.

Trust and disputes: number of appeals, time of decision, share of cancellations.

Transparency: share of games with an active "Check Round" button, coverage of certified proofs.

Roadmap 2025-2030

2025-2026 - Base: event bus, RTP monitor, VRF/sidelogs, Trust Score V1 with XAI cards, showcase for the regulator.

2026-2027 - Maturity: causal promo models, graph AML, RG explainability, mystery audit, export to unified formats.

2027-2028 - Default verifiability: periodic anchoring of hashes, public integrity reports, AI ≠ odds standard.

2028-2029 - Ecosystem: API for independent auditors/media, compatible event dictionaries, industry benchmarks.

2030 - Standard: "live" provider certificates, automatic inspections, licensing with continuous compliance.

Launch checklist (30-60 days)

1. Connect round/payout/promo/RG/AML events; include signatures and retention policies.

2. Configure the RTP monitor by windows and cohorts; make alerts and an investigation log.

3. Assemble VRF/commit-reveal-proofs; Add a Test Round button.

4. Deploy Trust Score V1: Math/Outcome/Promo/RG/Payments/Privacy/Operations.

5. Include "why flag" XAI cards and appeals process.

6. Add anti-gaming: mystery-selections, cross-sources, cumulative windows.

7. Launch dashboards for the operator/regulator and a short public honesty card for players.

Risks and how to reduce them

False flags: regular calibration of thresholds, "two-stage" checks, feedback for additional training.

Data/model drift: quality monitor, canary releases, version rollback.

Reporting manipulations: signatures, anchoring hashes, fines for retro edits.

Conflicts of jurisdictions: multi-level policies (Policy-as-Code) with feature flags.

Privacy: PII minimization, DLP, RBAC, short TTL and on-device for sensitive signals.

FAQ (short)

Can AI "decide" provider is dishonest?

AI flags anomalies and collects evidence with explanations; the final decision is for the person/regulator.

Does blockchain need?

Not necessarily. Enough signed logs; on-chain anchoring - optional for public verifiability.

Is it possible to "polish" the rating with promotions?

No: Trust Score takes into account incrementality and fines "stuffing" without effect or with hidden conditions.

AI makes the provider's integrity assessment substantive and verifiable. Instead of marketing promises - metrics and proofs, instead of disputes - explainability and appeals, instead of slides - live dashboards. The key principle is a rigid separation of the mathematics of games and AI-filing: no personal "twisting" of chances. This is how the market is built, where those who play by the rules win - and can prove it in one click.