AI integration platforms for personalization in online casinos

Introduction

AI integration in casino platforms opens up new opportunities: automatic selection of games, personalized offers and behavior forecasting. The main tasks are to increase retention and ARPU, avoiding intrusiveness and observing privacy.

1. Data collection and preparation

Events Tracking: Logging clicks, bets, winnings, sessions and rejections at Kafka/ClickHouse.
User Profiles: combining demographics, game history, spending and stock reaction into Customer 360.
Feature Store: feature engineering - average rate, frequency of visits, favorite providers.

2. Recommendation systems

1. Collaborative Filtering:

Matrix of players × games, calculation of similarities through ALS/SVD, issuance of "similar players played...."

2. Content-Based:

Evaluation of game attributes (RTP, volatility, theme) and matching based on user profile.

3. Hybrid model:

Combination of both approaches, ranking taking into account freshness and promotional priorities.

4. Frontend API:

'GET/recommendations/{ playerId}? limit = 10 '→ a list of games with relevance rating.

3. Dynamic bonuses and offers

Bonus personalization module:

Generation of individual offers: free spins, match deposits, cashback.

ML model:

XGBoost/LightGBM for predicting response probability and LTV, optimization of the offer for KPI.

Automation via Campaign Engine:

When creating a campaign, targeting based on 'predicted _ engagement> threshold'.

4. Predictive analytics and churn-prevention

Churn model:

Logistic Regression or neural network on a set of features: last session time, average win, bet frequency.

Trigger-actions:

Auto-distribution of re-engagement-offers at'churn _ score> 0. 7`.

Performance monitoring:

A/B tests with control and test groups, measuring lift in retention.

5. A/B testing and online training

Feature Flags:

Experiments at the level of recommendations and offers without code release.

Multi-armed Bandits:

UCB/Thompson Sampling algorithms for dynamic distribution of traffic between variants.

Metrics Pipeline:

Automatic calculation of p-value and confidence interval in BI.

6. Integration and Infrastructure

Microservices:

Separate services for Data Ingestion, Feature Store, Model Serving (TensorFlow Serving, MLflow).

Real-time Inference:

gRPC/REST endpoints with latency <50 ms, caching popular recommendations.

Batch Processing:

ETL via Airflow for daily retraining and model updates.

7. Privacy and security

GDPR/CCPA:

PII anonymization, legal mechanisms for deleting data on request.

Data Governance:

Retention, role access, audit models to avoid bias.

Secure ML Pipeline:

Data encryption at rest (at rest) and in transit (TLS), isolated environments for experts.

Conclusion