AI-driven Optimization of Casino Gaming Systems for Fraud Detection and User Behavior Analysis

The swift digitalization of casino gaming systems has brought with it advanced risks involving fraud and non-compliant behaviors, in addition to unparalleled possibilities for customer experience improvement through behavior analytics. Artificial Intelligence (AI) offers an exciting opportunity to maximize casino performance by allowing real-time fraud identification and predictive modeling of user activities. This paper examines the integration of AI algorithms, specifically machine learning (ML) and deep learning (DL) models, into casino management systems to counter security and engagement. We introduce an integrated approach blending supervised learning, anomaly detection, computer vision, and reinforcement learning to identify suspicious patterns, such as card counting, collusion, and account tampering, while profiling user behavior in parallel to personalize experiences and promote responsible gaming. Based on historical data from both web-based and physical-based platforms, the research analyzes the performance of AI models, such as decision trees, convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and autoencoders. Our findings indicate a 92.6% fraud detection accuracy and considerable enhancement in the user behavior clustering for adaptive promotional campaigns. The report brings to light the dual capability of AI in safeguarding game systems from illegal activities while generating business intelligence.