In a dynamic roundtable hosted by Gaming Eminence, three industry experts — Anton Antropov of Smartico.ai, Edmond Mitchell of Future Anthem, and Pantelis Theocharides of NuxGame — delve into the transformative impact of artificial intelligence on the iGaming sector. The discussion explores the next frontier of AI applications beyond current use cases, addressing real-time decision-making, personalisation, responsible gaming, and the challenges of building scalable AI architectures. The panelists share their insights on leveraging AI to redefine player experiences and operator strategies, highlighting innovative approaches that position their companies at the forefront of emerging trends.
Panelists:
Anton Antropov, Co-Founder & Chief Product Officer - Smartico.ai
Edmond Mitchell, Head of Data - Future Anthem
Pantelis Theocharides, Product Manager - NuxGame
Gaming Eminence: As AI continues to push the boundaries of real-time decision-making in iGaming, particularly with advancements in machine learning and neural networks, how do you envision the next frontier for AI in this space? Beyond current use cases, what novel applications or disruptive innovations do you foresee that could redefine player experiences and operator strategies?
Anton Antropov: "Artificial intelligence has already made significant strides in the iGaming industry, particularly in personalisation and fraud detection. However, its potential is poised to expand even further. Key areas where AI is expected to drive innovation include:
Real-time engagement: AI can dynamically analyse player behaviour to adjust gameplay on the fly. This could involve offering personalised rewards or modifying the number of games presented to keep the experience engaging and tailored to individual preferences.
Hyper-personalised promotions: Future iGaming platforms may generate experiences specifically customised for each player, adapting seamlessly to their style or mood in real time. This level of personalisation can enhance player satisfaction and loyalty.
Responsible gaming: AI plays a critical role in promoting responsible gaming by detecting risky behaviours and proactively intervening. This might be achieved through game adjustments or by offering personalised support to players who may be at risk.
Marketing optimisation: By analysing patterns in player behaviour, AI can refine marketing efforts by determining the ideal timing, channels, and content for outreach. Real-time segmentation of players based on behaviour enables more agile and targeted marketing strategies, improving overall effectiveness.
The industry's focus is increasingly aligning with these evolving trends, especially in enhancing real-time engagement, personalised promotions, and marketing optimisation. While classic machine learning techniques continue to be valuable, there is a growing exploration of generative AI to enhance capabilities in areas such as churn prediction, optimal communication timings, and lifetime value forecasting."
Edmond Mitchell: "Recent advancements in AI are driving a transformative shift towards end-to-end personalisation in iGaming. Personalising every step of the player journey—including tailored bet recommendations, real-time monitoring of jackpot sizes, and messaging the most receptive audiences—is becoming increasingly feasible due to these technological developments.
The potential for next-generation AI models to holistically personalise the gaming experience is particularly exciting. Significant work is being done on promoting positive play, aiming to encourage safe betting behaviours at optimal times through state-of-the-art machine learning approaches.
Agility in AI products is crucial, enabling rapid development, testing, and deployment to keep pace with emerging trends. This agility helps companies stay competitive while maintaining a focus on responsible gambling."
Pantelis Theocharides: "AI has immense potential to transform the iGaming industry. As the technology evolves, suppliers need to focus on innovation and adaptability to stay ahead. AI enhances personalisation, real-time decision-making, and player engagement—key drivers for next-generation gaming experiences. By investing in machine learning and predictive analytics, companies can better understand player behaviour, allowing for tailored experiences that dynamically adapt to each user. Developments like AI-powered content generation, real-time betting markets, and emotion-aware gaming offer new ways to engage players and encourage their continued participation.
Monitoring emerging trends and remaining flexible in implementing new technologies enable suppliers to deliver gaming experiences that meet current expectations while anticipating future needs. As AI continues to evolve, opportunities will increase to create more immersive, personalised, and responsible gaming environments, setting industry leaders apart in this rapidly changing landscape.
The future of AI focuses on creating dynamic, personalised gaming environments. Beyond current personalisation in marketing and offers, deeper customisation will be driven by real-time data analysis and behavioural predictions. Games could adjust in real-time based on a player's emotional state, skill level, and preferences, modifying difficulty, pacing, or narrative to create an evolving immersive experience. AI might also create personalised interfaces that adapt layout, visual style, and content suggestions, offering game recommendations or real-time tips based on individual play history.
AI-driven behavioural prediction will play a critical role in understanding players on a granular level. Machine learning models analysing user activity can identify behaviours that predict user churn, high engagement, or risky gambling patterns. Predictive algorithms could identify players at risk of problem gambling before dangerous patterns emerge. AI-driven interventions like nudges to take breaks or spending limits could promote healthier gaming behaviour.
Furthermore, AI-driven non-player characters or virtual companions might evolve to learn and adapt alongside players, creating immersive interactions that are both challenging and emotionally engaging. AI can also transform in-game betting markets by using real-time data to adjust odds and payouts based on live events and player behaviour. This adaptability could introduce instant micro-bets and dynamic markets within larger tournaments. For casual bettors, AI might offer personalised betting suggestions based on risk tolerance, favorite teams or players, and even current mood.
As the industry faces increasing regulatory scrutiny, AI will be central to identifying cheating and fraud in real-time. Advanced machine learning algorithms can detect suspicious behaviours or irregular betting patterns instantly, allowing operators to act against cheaters promptly. AI could also automate compliance with regional laws and offer experiences tailored to local regulations."
Gaming Eminence: In an industry where data is king, the integrity and quality of data used in AI models are crucial. Considering the diverse and dynamic nature of player behavior data, how do you ensure your AI systems remain agile and adaptive to changes? Can you discuss any innovative approaches or frameworks you've adopted to mitigate biases, enhance data diversity, and maintain model relevance over time?
Edmond Mitchell: "An effective data platform must efficiently store and access data while being capable of ingesting diverse data sources, whether they are streaming in real-time or processed in batches. As new data sources emerge continually, the data model should be flexible enough to integrate these sources and link relevant information seamlessly.
By building analytical models exclusively on behavioural data without incorporating any personally identifiable information (PII), it's possible to identify risks through unusual changes in wagering patterns and bring that data to attention immediately. This method ensures privacy and applies uniformly to all players, regardless of demographics, promoting fairness and compliance with data protection regulations. However, operators can still design appropriate responsible gambling strategies for groups that have been identified as needing higher levels of protection, tailoring interventions without compromising individual privacy."
Pantelis Theocharides: "In the data-driven industry, ensuring the integrity and quality of data used in AI systems is crucial. The constantly evolving and diverse nature of player behaviour necessitates that AI models remain agile, adaptive, and reflective of real-world trends.
Several innovative approaches can enhance data diversity and mitigate biases in AI models. One key strategy is continuous data monitoring and feedback loops. Regularly updating models with fresh data allows systems to evolve alongside player behaviour, capturing shifts in preferences and patterns in real-time. This practice maintains the relevance of AI-driven insights, keeping them sharp and aligned with current trends.
Rigorous testing and validation methods are essential to identify and eliminate biases in AI models. Ensuring that training data reflects a broad and diverse range of players minimises the risk of skewed outcomes that could impact personalisation or decision-making processes. Focusing on diverse data sources helps in building models that are not only more robust but also fairer and more inclusive.
These strategies align with the principles of responsible gaming and fostering long-term player trust. By ensuring AI systems are transparent and responsive to changes in player behaviour, gaming environments can be created that are engaging while promoting fairness and well-being. Central to this approach is building trust with players, ensuring their data is used responsibly to enhance their experience rather than exploit it. As the industry evolves, maintaining a focus on data integrity and responsible gaming remains essential."
Anton Antropov: "You've highlighted some key challenges in the development of AI models for gambling platforms. When comparing in-house AI models to those provided by external companies, both approaches offer their own advantages and trade-offs.
Developing robust ETL (Extract, Transform, Load) processes is crucial for extracting and ensuring the integrity of data from diverse platforms. Integration with numerous companies means that data flows, business concepts, and structures can vary significantly. Building a standardised approach to support this diversity is a considerable challenge, one that is less pronounced when building AI in-house and targeting only the specifics of a single platform.
However, working with large datasets—billions of data points across different providers and markets—offers significant benefits. It provides better visibility into behavioural patterns, allowing for testing of assumptions and models on vast datasets. Once optimised, these models can function effectively for new clients, even those with a small user base.
A broad client base also offers varied business perspectives on model usage. Actively collecting feedback from clients helps in continuously improving the product and sharing those improvements within the business community.
To maintain model relevance, there is a process for continuously monitoring each model’s KPIs. Models naturally tend to degrade over time, some faster than others. As soon as a decline in performance is noticed, the model is fine-tuned or retrained to ensure it remains effective.
The entire process requires coordination among many teams within a company. Success managers maintain close contact with clients, business analysts and data scientists handle KPIs and model quality, integration teams manage the ETL processes and data integrity, and DevOps and R&D teams connect all the dots to ensure the model's performance.
By leveraging AI for real-time engagement and hyper-personalised promotions, operators can proactively detect risky behaviours and intervene early. This could include adjusting game settings or offering personalised messages with support to players showing signs of problematic gambling.
Focusing on continuously monitoring model performance and maintaining data integrity allows for adaptability to evolving player behaviours."
Gaming Eminence: Predictive analytics in iGaming is evolving rapidly with AI, particularly in areas like player retention, fraud detection, and risk management. How have you leveraged unconventional AI techniques or interdisciplinary approaches to uncover new insights or develop predictive models that offer a competitive edge? Can you share an example where thinking outside the traditional AI toolbox has led to significant breakthroughs in your operations?
Pantelis Theocharides: "Predictive analytics in the iGaming industry is advancing rapidly, particularly in areas like player retention, fraud detection, and risk management. To stay ahead, it's important to think beyond the traditional AI toolbox, especially when it comes to understanding player behaviour.
One unconventional approach involves blending AI with behavioural psychology. Instead of relying solely on numbers and patterns, this method dives into the underlying reasons why players make certain choices. For example, rather than just looking at how long a player spends in a game, it analyses emotional triggers, satisfaction levels, and even in-game frustrations. This deeper understanding helps anticipate when a player might disengage, allowing for intervention before they leave the platform.
A practical example of this is revamping strategies for player retention. AI is applied to identify subtle patterns, like when players tend to quit after losing a few matches or after failing to level up for a while. The difference comes from adding a behavioural layer to this data. Offering personalised challenges or rewards at the right time—not just based on activity but also on play style and preferences—can have a huge impact on keeping players engaged.
This blend of predictive AI and behavioural insights can significantly boost retention by providing more meaningful, timely interventions that resonate with individual players. By thinking beyond traditional AI and adopting a more holistic view of player behaviour, it's possible to gain a competitive edge in keeping players more engaged and satisfied over the long term."
Anton Antropov: "Assessing whether we're genuinely 'thinking outside the box' is challenging, as such evaluations are often best made by external observers. We've never considered AI to be a one-size-fits-all solution; it's just another tool in our toolkit. In some cases, deploying AI can feel like using a hammer when what we really need is a screwdriver.
AI is merely one component of our product strategy. While we're not hyping its potential, we're keen to explore areas where it can enhance both our internal processes and the product itself. Some ways we're considering employing AI include:
Mining our knowledge base and support tickets with generative models.
Enhancing our business intelligence tools with a conversational interface for analytical queries.
Creating marketing materials using generative AI.
Automating content translation and proofreading.
Monitoring key metrics to detect anomalies.
That said, significant challenges remain when integrating AI into regulated environments. Can we truly trust AI? Are we confident it will place a comma correctly in a highly personalised marketing message? These are important questions that need careful consideration."
Edmond Mitchell: "Effectively addressing issues often involves examining them from multiple perspectives. Approaching problems uniquely can lead to better solutions. For instance, we're improving our methods of handling problematic play by proactively engaging with players before any signs of risk appear. This comprehensive strategy helps operators support players in gambling safely and responsibly."
Gaming Eminence: The integration of AI across various functions in iGaming—from game design to customer interaction—requires not just a robust infrastructure but also a forward-thinking mindset. How do you approach the challenge of creating an AI architecture that is both scalable and future-proof, particularly when considering the fast-paced evolution of AI technologies?
Anton Antropov: "In the rapidly evolving field of AI, adopting an agile approach to architecture is crucial. Flexibility allows for quick pivots as technologies and needs change, avoiding rigid structures that may soon become obsolete.
A key focus should be on data management—implementing robust processes for collecting, cleaning, and ensuring data is easily accessible. Maintaining high-quality data, along with effective monitoring systems, is essential for future-proofing AI architectures."
Edmond Mitchell: "Acknowledging that it's nearly impossible to future-proof a platform in the rapidly evolving tech landscape, focusing on agility and adaptability is essential. Amplifier AI's ecosystem is designed for rapid development and deployment, facilitating real-time personalisation with minimal latency between player behaviour and AI-driven recommendations.
A key aspect of our approach is a real-time data architecture that processes and reacts to millions of data points with split-second precision. In areas such as jackpot monitoring, bet recommendations, and sports betting, this capability enables real-time interventions that significantly impact player experiences.
Additionally, the ability to combine multiple models into an ensemble of AI-driven tools—customisable by each operator—is crucial. This multi-model strategy provides flexibility for applications like retention campaigns, player acquisition, and real-time marketing, allowing operators to personalise every step of the player journey."
Pantelis Theocharides: "Integrating AI across various aspects of iGaming—from game design to customer interaction—requires more than just robust infrastructure. It demands a forward-thinking approach capable of keeping pace with the rapid evolution of AI technologies. Building an AI architecture that is both scalable and future-proof necessitates a focus on flexibility and innovation.
Designing AI systems to be modular is essential. Instead of constructing a rigid structure suited only for today's needs, creating one that can easily evolve as new AI technologies emerge allows for quick integration of new tools or capabilities without overhauling the entire system. For example, whether upgrading machine learning models or incorporating real-time data analytics, a modular architecture enables smooth adaptation as the industry grows.
Embracing a hybrid approach by combining cloud-based AI with on-premise infrastructure offers the best of both worlds. Scalability through the cloud ensures handling large data volumes and processing power, while maintaining control and security through local systems. This balance provides the flexibility to scale up or down based on demand without sacrificing performance or data integrity.
Investing in real-time AI processing can significantly enhance player experiences. By implementing tools that analyse player data and behaviours as they happen, it's possible to personalise experiences on the fly—whether adjusting game difficulty, offering customised rewards, or enhancing customer support interactions. Focusing on real-time adaptability can set a new standard for AI in iGaming, enabling responsiveness to player needs and preferences moment by moment.
By building an AI architecture that is flexible, scalable, and ready for the future, suppliers can stay ahead of the curve as AI technology advances. This approach not only meets today's demands but also allows for the easy adaptation of exciting innovations on the horizon."