How AI helps run casinos in real time

Why "real time" casinos and where is AI

Bet, stream, box office, bonus - everything happens here and now. AI allows:

pick up a game/offer for this particular player at that moment;
see the risk in advance (RG/fraud/KYT) and stop the dangerous transaction;
keep the quality of the stream by switching the protocol/bitrate before the player's video "freezes";
distribute the load and money: payment limits, PSP selection, cache warming and autoscale.

Real-time AI Backbone Architecture

Data flow (≤1 -3 s):

SDK → tire of events (Kafka/NATS) → stream-enrichment → Feature Store (online) → Decisioning API (scoring of ≤100 ms) → actions (personalisations/limits/routes) → result telemetry.

Contracts:

Event (minimum): 'event', 'ts (UTC)', 'playerId', 'sessionId', 'traceId', 'geo', 'device', sums as decimal + 'currency'.
Solution (minimum): 'decisionId', 'modelVer', 'featureVer', 'latency _ ms', 'policy' (which guardrail worked), 'explanation' (top features).

Real-time Solution Key Outlines

1) Personalization of lobbies and offers

Models: hybrid of recommendations (game/player embeddings) + online rerank for the current context (device, local, session, time budget).

Solution: Games/banners list and "next best action" (NBO).

SLO: p95 response ≤80 -100 ms, fault tolerance - degradation to the default rule.

2) Responsible Gaming (RG) - security tips and blocks

Signals: rate of bets, "dogon," night cycles, long sessions, cancellations of conclusions.

Solution: "prompt pause" → "show limit" → "temporary stop" (escalation).

Rule: model recommends, policy decides; all blocks - with explanation and log.

3) Fraud/AML/KYT and box office

Online: payment/output scoring, device/card/address link graph, on-chain address risk; idempotency by 'requestId'.

Solution: 'approve	step-up	hold	decline ', select PSP route (cost/availability/country).
SLO: ≤150 ms, FPR under control, manual appeal.

4) Live stream quality (QoS)

Signals: WebRTC-RTT, dropped frames, bitrate, network type.

Solution: profile/protocol switching (WebRTC ↔ LL-HLS), side-bets limitation during degradation, soft resync of bet timers.

Goal: Reduce rebuffer and 'aborted _ rounds' without UX'trimming'.

5) Operating circuit of SRE/scale

Forecast + nowcast: RPS, payout queue, bridge/wallet load.

Solution: turn on HPA, warm up cache/CDN, switch provider, slow down "heavy" features.

Guardrails: Do not touch a financial writer without manual confirmation.

Safe "online learner": Bundles and RLs with insurance

Banding/conservative RL only in money-safe zones (content ranking, banner order).

Restrictions: frequency of impressions, prohibition of "pressing" messages (RG), bonus budget per user/day.

Incrementality check: uplift metrics, CUPED/AA tests.

Examples of protocols (simplified)

Solution Request (Personalization):

json
POST /rt/decision/lobby
{
"decisionId":"d_7f3",  "playerId":"p_82917",  "ctx":{"locale":"de-DE","device":"android_web","country":"DE"},  "features":{
"7d_launches":14,"avg_bet_7d":"1. 80","currency":"EUR",   "night_ratio_30d":0. 37,"fav_providers":["pragm","pgsoft"]
}
}

Answer:

json
{
"items":[
{"gameId":"pragm_doghouse","rank":1},   {"gameId":"pgsoft_moonrise","rank":2}
],  "explanation":["fav_providers","7d_launches"],  "modelVer":"rec_2. 9","featureVer":"fs_1. 4","latency_ms":42
}

RG solution (naj):

json
{
"action":"show_break_prompt",  "reason":"high_session_intensity",  "policy":"rg_v3",  "modelVer":"rg_1. 6",  "cooldown_min":60
}

Payment route:

json
{
"decision":"approve_step_up",  "route":"psp_b_alt",  "explanation":["device_link_density","recent_chargeback_region"],  "modelVer":"fraud_4. 1","latency_ms":87
}

Observability and quality of AI online

SLI/SLO for solutions: p95/99 latency, error-rate, fallback-rate.

Business metrics: CR lobby→game, churn, ROI of offers, share 'step _ up/decline', rebuffer-ratio.

ML-observability: drift feature/scoring, freshness feature, share of empty features, distribution by segments (country/channel/device).

Audit: 'decisionId', 'modelVer', 'dataVer', 'featureVer', explanations - save with the action.

Guardrails, Ethics and Compliance

Rule priority: Money/RG/AML decisions are "rule over model."

PII minimization: online - pseudonyms; PII lives in a separate perimeter.

Communication frequency: limits per day/week; ban on offers from tired/high-risk segments.

Explainability: person-in-circuit for controversial rejections; understandable reasons for the player.

Logs are immutable (WORM), versions of policies ('policyVer') and models are for audit.

Anti-patterns

"Black Box" in RG/AML without explanation and right of appeal.

A single "speed" for everything (personalization, fraud, RG) is a conflict of goals and mistakes.

Online models without degradation to the rules → SLO drop with lag feature.

OLTP is mixed with online features/scoring in one database - an increase in rate latency.

Lack of idempotency ('requestId') at the checkout/payments/webhooks.

Experiments without incrementality are "beautiful" gains that do not give ROI.

Casino real-time AI launch checklist

Data and features

Single event contract (UTC, decimal money, 'traceId').
Online Feature Store (TTL, backfill, freshness monitoring).
Degradation channels with empty/old features.

Models and Solutions

SLO: p95 ≤100 ms (personalization), ≤150 ms (fraud/cash register).
Canary calculations, A/B and uplift, explicit guardrails.
Explainability + 'modelVer/dataVer/featureVer' in each response.

Integrations and Actions

Idempotency ('Idempotency-Key '/' requestId') and retrai.
PSP/QoS routes/offers - managed by flags, decision logs - in WORM.
Rollback and kill-switch protocols for each zone.

Observability and safety

latency/error/fallback dashboards + business metrics.
Drift/quality-gates, alerts by segment.
RG/AML policies over models, communication frequency limits.
PII isolation, role access, all solutions log.

Real-time AI is a casino operating system: it takes micro-decisions hundreds of times per second, but according to pre-agreed rules and with measurable benefit. Combine streaming features, fast scoring, hard guardrails and observability - and you get managed revenue growth, sustainable SLOs and risk mitigation, while remaining correct with players and regulators.