How AI is transforming the Sportsbook industry: Part 1

The impact of AI on the sports market will be staggering, with projections showing that it will reach USD 29.7 billion by 2032, growing at a CAGR of 30.1% from 2023. (Source: Allied Market Research)

At the heart of this revolution lies the rapidly evolving sportsbook industry, where AI is not just a tool, but the very engine of transformation.

AI driven innovations are reshaping how sportsbooks operate through real-time odds adjustments, fraud prevention, and personalized marketing, thereby enhancing user experiences, and driving profitability.

With AI, Sportsbook applications can but are not limited to:

• Optimize pricing models with real-time data analysis
• Enhance security with fraud detection and risk management
• Deliver 24/7 AI powered customer support
• Predict player behavior to improve retention and engagement

In this blog, we will explore the most impactful AI use cases for sportsbooks, their implementation strategies, tools, and best practices. We have chosen these use cases based on their high ROI impact and compliance friendliness. However, we do not claim that these use cases comply with every jurisdiction. We strongly recommend conducting a compliance assessment specific to your jurisdiction.

This blog is part of our ongoing series, ‘Building Modern Sportsbook’. In earlier parts, we explored the application architecture and tech stack that power modern scalable Sportsbook platforms. If you missed any of those, feel free to explore here.

The power of AI: Top Sportsbooks use cases for better ROI

Implementing AI for various use cases can significantly enhance an iGaming operator's ROI in their Sportsbook application, though there are multiple other use cases to explore.

AI for odds and dynamic pricing

Imagine a Sportsbook platform that’s constantly learning - not just from individual matches, but from thousands of games, historical outcomes, and the ever-evolving pulse of betting trends. Behind the scenes, AI engines are tirelessly analyzing live match events as they unfold, correlating them with years of historical data and real-time user behavior across the platform.

When critical moments occur, such as a sudden red card or a surprising goal, machine learning based pricing models spring into action. These models, trained on vast datasets, instantly reassess the probabilities and adjust the odds with precision. It’s not just about reacting quickly, but it’s also about making calculated, data-driven decisions that factor in market sentiment and statistical probability.

This results in Odds that are not only more accurate but also more competitive, giving players a fair and engaging experience while ensuring the operator keeps risk in check. It’s the perfect balance of intelligence and agility powered by AI.

Implementation strategy

• Stream real-time sports data and bet history into a centralized data pipeline.
• Train ML models (e.g., regression or gradient boosting) on historical and live data to predict outcome probabilities.
• Integrate these models into the odds engine to dynamically adjust pricing during pre-match and in-play events, while continuously learning and refining based on market behavior.

Data sources

Sportsbook Bet History, Sportradar APIs, in-game events.

Tech stack

Kafka (real-time data streaming), XGBoost (predictive pricing models), AWS SageMaker or equivalent ML platforms for model training and deployment.

Third-Party Solutions vs. In-house

Custom AI models and third-party providers (e.g., Genius Sports) both have their merits. While custom models offer greater flexibility and potential for differentiation, commercial off-the-shelf solutions can accelerate implementation and may bring domain expertise. Choice is dependent on the operator’s strategy, resources and time to market goal.

ROI impact

• Faster odds adjustments bring the competitive edge
• Improved risk balancing may result in high profit margins

AI-powered risk and fraud detection

In the fast-paced world of online betting, not every action is what it seems. A sudden surge in high-stake bets, unusual login behavior, or a player placing identical bets across multiple accounts could signal something more than just a lucky streak. Behind the scenes, AI engines can constantly monitor the platform, analyze betting patterns and user behavior in real time.

These intelligent systems can quickly and accurately detect anomalies like arbitrage betting, potential account takeovers, or irregular cash flow. When something “feels off”, the system raises a flag, enabling operators to intervene proactively. More than just fraud prevention, this also ensures the platform upholds responsible gaming standards, protecting both the business and the players it serves.

Implementation strategy

• Aggregate real-time behavioral and transactional data across the player lifecycle.
• Apply a hybrid approach using supervised models to catch known fraud patterns and unsupervised techniques to uncover emerging threats.
• Integrate this intelligence into a rule engine or alert system that enables immediate action and ongoing refinement through feedback loops.

Data sources

Player IP address, activity patterns such as login, deposit, betting, etc.

Techniques/Models

Supervised learning models e.g. XGBoost to detect known patterns and unsupervised learning to detect unknown fraudulent activities.

COTS vs. In-house

Here we advise starting with readymade solutions available e.g. SEON, Group-IB, etc. This would help with the industry-standard security on the platform in compliance with regulations.

ROI impact

• Reduced fraud losses help protect Sportsbook revenue
• Automated compliance checks enable faster regulatory approvals

AI-driven player retention and churn prediction

Consider a player who used to bet regularly but hasn’t logged in or made any deposits lately. Instead of missing these early signs of churn, AI can help flag this change in behavior so the team can take timely action. By analyzing subtle behavioral shifts like reduced session times, smaller bet amounts, fewer interactions, it identifies early warning signs of churn.

The role of AI doesn’t end there. It also predicts the likelihood of disengagement and immediately triggers personalized engagement strategies. It could be a tailored promotion, a check-in message, or content recommendations aligned with the player’s past interests. This proactive approach not only improves retention but also shows players that the platform understands them, and wants them to stay engaged, responsibly.

Implementation strategy

• Integrate player behavioral telemetry into a unified data system (UDS).
• Build machine learning pipelines that continuously monitor engagement trends.
• Use predictive churn models to segment at-risk users and trigger personalized retention workflows, such as personalized bonuses, targeted messages, or content nudges, through automated marketing systems or CRM tools.
• Focus on agility and experiment tracking to iteratively improve model precision and ROI impact.

Data Sources

Player profile and segmentation data (e.g. registration date, location, age group, etc.), Betting & Gameplay activity data (Bet frequency, average bet size, etc.), Session metrices (e.g. session frequency, duration, inactivity gaps, etc.), and promotion/bonus interaction logs, etc.

Techniques/Models

Classification models (Random Forest/XGBoost) to predict churns coupled with Deep Learning (LSTM - Long Short-Term Memory) for sequential betting pattern analysis and personalized recommendations.

Third-Party Solutions vs. In-house

We recommend developing an in-house system by leveraging in-house Unified Data System and cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, or Google Vertex AI) for model training.

ROI Impact

• Lower churns result in higher Lifetime Value (LTV)
• Personalized offers results in better player engagement

We hope you enjoyed this exploration on how AI is transforming the Sportsbook industry. There is still a lot more to explore! In the next part, we'll dive into additional AI use cases and share some valuable best practices. Stay Tuned!

Are you ready to future-proof your Sportsbook with AI? Let’s keep this conversation going and explore how AI can take your platform to the next level! Connect with us now!