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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Suggesting games based on player preferences and past gaming history

Introduction

The gambling industry has always been at the forefront of embracing new technologies to enhance customer experiences and improve business operations. In recent years, the advent and maturation of Machine Learning (ML) and Artificial Intelligence (AI) have provided new opportunities for the industry to innovate and optimize. One area where AI has shown substantial promise is in the sphere of game recommendation based on player preferences and past gaming history. By using complex algorithms and predictive models, these systems can analyze a player's habits, preferences, and past gaming history to suggest games that they would likely enjoy and engage with.

Challenges

Despite this potential, the implementation of AI and ML in the gambling industry is not without its challenges. These include data privacy concerns, the need for high-quality, accurate data to feed into the algorithms, overcoming the inherent unpredictability of human behavior, and the ethical implications of using AI in an industry that can contribute to problem gambling. Furthermore, there is the challenge of ensuring that the AI systems are transparent and explainable, to maintain player trust and to meet regulatory requirements. The lack of standardized platforms and the high cost of developing and implementing custom AI solutions can also be a barrier for many operators in the industry.

AI Solutions

Despite these challenges, several AI solutions have been developed and implemented to tackle them. For instance, BetBuddy, a responsible gambling solution owned by Playtech, uses AI to identify at-risk gambling behavior and provide targeted interventions. It uses a combination of ML and behavioral science to create a player-centric view of risk. Similarly, Kindred Group has launched a system that uses player data and machine learning algorithms to detect early signs of problem gambling. Other companies like Scientific Games and IGT have developed AI-driven recommendation engines that suggest games based on a player's past gaming history and preferences. These systems use complex algorithms to analyze patterns in player behavior and predict what games they would likely enjoy.

Benefits

The benefits of using AI and ML in the gambling industry are manifold. First and foremost, it allows for a more personalized gaming experience, which can enhance player engagement and loyalty. By suggesting games that are more aligned with a player's preferences, operators can increase the likelihood of repeat play and player retention. Furthermore, it can enhance responsible gambling efforts by identifying at-risk behavior and providing timely interventions. AI can also streamline operations and improve business efficiencies, by automating tasks and providing data-driven insights for decision-making. Lastly, it can provide a competitive edge in a highly saturated market, by offering a unique, personalized gaming experience.

Return on Investment

The return on investment (ROI) from implementing AI and ML in the gambling industry can be significant. According to a study by Juniper Research, AI is expected to save the betting and gaming industry up to $1 billion annually by 2021, through reduced fraud and improved operational efficiency. Furthermore, a report by EY found that personalized experiences, driven by AI, can increase customer profitability by up to 15%. This is in addition to the potential revenue increase from enhanced player engagement and retention. However, the exact ROI will depend on the specific implementation and how effectively the technology is leveraged.

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