Crypto Casino
Torrent Gear
AI modeling of player behavior and preferences
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A player is a sequence of micro-decisions: go in, pick a game, place a bet, stop, come back. AI allows you to turn these signals into predictions (retention, outflow, LTV), recommendations (games/missions/bonuses) and preventive measures (limits, pauses, RG alerts). The goal is not to "squeeze out metrics at all costs," but to find a stable balance: growing business value and player safety.
1) Data: what to collect and how to structure
Events:- Sessions (in/out time, device, traffic channel).
- Transactions (deposits/withdrawals, payment methods, currencies, delays).
- Game actions (bets/winrate, slot volatility, RTP by provider, game change frequency).
- Marketing (offers, campaigns, UTM, reaction).
- Behavioural RG signals (rate of rate build-up, night sessions, "chasing loss").
- Social/community signals (chat, tournament/mission participation, UGC).
- Event Streaming (Kafka/Kinesis) → cold storage (Data Lake) + display cases (DWH).
- Online feature store for real-time scoring.
- Single keys: player_id, session_id, campaign_id.
2) Fici: building set of signals
Units and frequencies:- RFM: Recency, Frequency, Monetary (for 1/7/30/90 days).
- Pace: Δ deposit/bet/time in the game (MoM/DoD).
- Rhythm of sessions: hour/day cycles, seasonality.
- Taste profile: providers, genres (slots, live, crash/aviator), volatility rates.
- "Cognitive" complexity: speed of decision-making, average session length to fatigue.
- N-grams of games (transitions "igra→igra").
- Time chains: passes, "loops" (return to your favorite game), reaction to promo.
- Abnormal growth of deposits, "Dogon" after losing, night marathons.
- Self-exclusion/pause triggers (if enabled), bonus "selection" speed.
3) Tasks and models
3. 1 Classification/scoring
Churn: logistic regression/gradient boosting/TabNet.
Fraud/multi-acc: isolation forest, graph models of connections, GNN for devices/payment methods.
RG risk: anomaly ensembles + threshold rules, legal calibration.
3. 2 Regression
LTV/CLV: Gamma-Gamma, BG/NBD, XGBoost/LightGBM, transactional sequence transformers.
ARPPU/ARPU forecast: gradient boosting + calendar seasonality.
3. 3 Sequences
Game recommendations: sequence2sequence (GRU/LSTM/Transformer), session item2vec/Prod2Vec.
Time forecast of activity: TCN/Transformer + calendar features.
3. 4 Online orchestration
Contextual bandits (LinUCB/Thompson): choosing an offer/mission in a session.
Renewal Learning (RL): "hold without overheating" policy (reward = long-term value, RG risk/fatigue penalties).
Rules over ML: business restrictions (you cannot give an offer in a row N times, mandatory "pauses").
4) Personalization: what and how to recommend
Personalization objects:- Games/providers, betting limits (comfort ranges).
- Missions/quests (skill-based, without cash prize - points/statuses).
- Bonuses (freespins/cashback/missions instead of "raw" money).
- Timing and communication channel (push, e-mail, onsite).
- "Mixed sheet": 60% personally relevant, 20% new, 20% safe "research" positions.
- Without a "tunnel": always a button "random from selected genres," a block "return to...."
- Soft hints: "it's time to take a break," "check the limits."
- Auto-hiding of "hot" offers after a long session; priority - missions/quests without bets.
5) Anti-fraud and honesty
Device/payment graph: identifying "farms" with common patterns.
Risk scoring by payment method/geo/time of day.
A/B protection of promotional codes: mouthguards, velocity limits, "promo hunting" detector.
Server-authoritative: critical progress and bonus calculations - only on the backend.
6) Architecture in production
Online layer: event flow → fichestore → online scoring (REST/gRPC) → orchestrator of offers/content.
Offline layer: model training, retraining, A/B, drift monitoring.
Rules and compliance: policy-engine (feature flags), "red lists" for RG/AML.
Observability: delay metrics, scoring SLAs, tracing decisions (reasons for issuing an offer).
7) Privacy, ethics, compliance
Data minimization: only required fields; PII - in a separate encrypted loop.
Explainability: SHAP/exhaustive reasons: "the offer is shown because of X/Y."
Fairness: age/region/device bias check; equal thresholds of RG interventions.
Legal requirements: personalization notifications, opt-out option, storage of decision logs.
RG priority: if the risk is high, personalization switches to "restriction" mode, not "stimulation."
8) Success metrics
Product:- Retention D1/D7/D30, frequency of visits, mean length of healthy session.
- Conversion to target activities (quests/missions), catalog depth.
- Uplift LTV/ARPPU by personalized cohorts.
- Efficiency of offers (CTR/CR), share of "blank" offers.
- RG incidents/1000 sessions, proportion of voluntary pauses/limits.
- False Positive/Negative anti-fraud, time to detection.
- Complaints/appeals and their average processing time.
- Drift feature/target, retrain frequency, offline→online degradation.
9) Implementation Roadmap
Stage 0 - Foundation (2-4 weeks)
Diagram of events, showcases in DWH, basic fichester.
RFM segmentation, simple RG/fraud rules.
Phase 1 - Forecasts (4-8 weeks)
churn/LTV models, first recommendations (item2vec + popularity).
Dashboards of metrics, control holdout.
Stage 2 - Realtime Personalization (6-10 weeks)
Orchestrator of offers, contextual bandits.
Online experiments, adaptive mouthguards by RG.
Stage 3 - Advanced Logic (8-12 weeks)
Sequence models (Transformer), segments of inclinations (volatility/genres).
RL policy with "safe" fines, graph anti-fraud.
Stage 4 - Scale (12 + weeks)
Cross-channel attribution, mission/tournament personalization.
Autonomous "guides" for the responsible player, pro-tips in the session.
10) Best practices
Safety-first by default: personalization should not increase risks.
"ML + rules" hybrid: business constraints over models.
Micro experiments: fast A/B, small increments; fixation guardrails.
UX transparency: Explanations to the player "why this recommendation."
Seasonality: retraining and re-indexing the catalog for holidays/events.
Synchronization with support: escalation scenarios, visibility of offers and metrics in CRM.
11) Typical errors and how to avoid them
Offline scoring only: without online personalization "blind." → Add fichestore and realtime solutions.
Overheating by offers: short uplift, long harm. → Frequency caps, "cooling" after sessions.
Ignoring RG signals: regulatory and reputation risks. → RG flags in each solution.
Monolithic models: difficult to maintain. → Microservices by tasks (churn, recsys, fraud).
No explanation: complaints and blocks. → Logs of reasons, SHAP-slices, reports for compliance.
12) Launch checklist
- Event dictionary and uniform IDs.
- Fichestor (offline/online) and SLA scoring.
- Churn/LTV Base Models + Recommendation Showcase.
- Orchestrator of offers with bandits and guardrails RG.
- Dashboards of product/business/RG/fraud metrics.
- Privacy, explainability, opt-out policies.
- Retrain process and drift monitoring.
- Runbooks incidents and escalation.
AI modeling of player behavior and preferences is not a "magic box," but a discipline: high-quality data, thoughtful features, appropriate models, strict safety rules and continuous experiments. The combination of "personalization + responsibility" wins: long-term value grows, and players get an honest and comfortable experience.