When online gaming was first launched, casino platforms were essentially game hosting pages that gamers could visit to play games, and nothing more. As access to technology and the capability of technological devices improved, these platforms evolved to enable the storage of user data, access to loyalty programs, and speedy payment systems. Yet, nothing has been as revolutionary to online gaming (esp. Roulette games) as the adoption of artificial intelligence (AI) and machine learning (ML) tools.
There are two timelines of AI and ML adoption in online roulettes: before and after the launch of consumer large language models (LLMs), and there is no better way to grasp the scale of the change as of 2025 than by analysing what AI and ML in online roulette looked like between both timelines.
Then and Now: The Journey of Artificial Intelligence (AI) and Machine Learning (ML) in Online Roulette
At the core, before the launch of LLM AI tools like ChatGPT, the utilization of AI and ML in online roulettes was for analytical tasks, and their manner of deployment was mostly static, whereas since 2022, and especially in 2025, AI is being utilized dynamically; able to adapt experiences for specific customers, while operators also use it to predict behaviours. Below is a detailed breakdown of how this works:
Player Behaviour Tracking & Personalisation
Before the introduction of LLMs, AI was used to segment users based on rules (casual users vs. VIPs), roulette variants or bonuses were recommended solely based on historical behaviour, and personalisation was mostly one-directional and static.
In 2025, game personalisations are now real-time and adaptive, based on LLM-driven insights. Roulette interfaces adjust game speed, layout, or dealer interaction based on the player’s engagement. Also, LLMs have enabled human-like interactions with players, like in the prompts and suggestions.
Betting Strategy Assistance and Predictive Analytics
Interactive AI assistants now simulate roulette strategies with users, including betting patterns based on session behaviour (e.g., increasing wager patterns, risk tolerance), and even explain probabilities, house edge, and psychological traps in human-friendly terms.
In the past, basic ML used to show “hot” and “cold” numbers only. There was no real intelligence, as the data that was shown often lacked context or predictive ability, while simulation tools were largely limited, existing mostly in forums or spreadsheets.
Fair Play, RNG Auditing & Fraud Detection
Before the introduction of LLMs, RNG fairness tests were done manually or in batches, pattern-based fraud detection often generated false positives, and machine learning was used to detect anomalies in gameplay by trying to spot bot patterns or cases of collusion.
In 2025, AI models audit live roulette games in real time. Smart vision tools assess dealer hand movements for fairness (especially in live roulette), and LLms assist in detecting and explaining complex fraud chains across user accounts.
Responsible Gaming Monitoring
Before LLMs, platforms flagged problematic behaviours based on deposit size, session time, or win/loss streaks. Now, LLMs can apply context to converse with users showing risky behavior, nudging them gently. Also, because the warnings lacked context, the former technology did not always address real needs, as high-volume bets do not always mean problem gambling. The auto-tiggered warnings or cool-off prompts, therefore, were often ignored. Now, however, limit recommendations are dynamic and based on behavioral tone and engagement styles of users.
Customer Support and Player Engagement
Customer support is the most advanced feature to be impacted by AI and ML breakthroughs from LLMs. Previously, chatbots were rule-based (e.g., “Type 2 for bonus help”), while chats lacked contextual understanding of emotion, urgency, or sarcasm. In fact, before now, everyone hated to use the chatbots, and only followed the process to reach the point where they could request a human customer service agent.
Now, LLM-powered bots handle natural conversations, explain odds, and handle disputes and customer frustrations intelligently. AI can role-play as dealers, explain roulette rules mid-game, and adjust tone based on the player’s mood. LLMs in 2025 are also multilingual, emotion-aware, and highly scalable.
Game Design and User Experience
Before LLMs, AI tested different roulette layouts and UI designs in A/B experiments, game development was manual and slow to iterate, and content (bonus screens, dealer scripts, and game narration) was static. In 2025, generative AI now create dynamic roulette wheel variations, AI-written dealer scripts, and personalized game modes. AI now enables personalized roulette experiences (players can design their wheels and set bet types). With regards to game layouts, AI tests thousands of UI combinations and adapts in real-time based on user fatigue, bets, and emotions.
Marketing and Player Acquisition
AI helps casino operators to predict who will churn, why they will churn, and suggest effective ways to intervene with the right emotional tone. Also, customer acquisition is no longer largely based on Meta/Google-sourced lookalike data. Casinos can now create hyper-personalized marketing messages based on behavior and demographics.
