The gaming market is changing rapidly. With the shift to online and mobile gaming, and now with the advent of new immersive technologies like augmented reality and virtual reality, operators face intense competition to attract customers, with continuous innovation. The key to your success. But in such an environment, new problems will arise and new solutions will have to be found. Mainly due to the proliferation of Internet-connected devices, operators need to be aware of how accessibility in abundance can pose a risk to some people. Experian believes that machine learning can be useful in two particular areas. Namely, the protection of minors and the identification of problematic players.
Protect the young
As most parents know, teens now have access to a multitude of Internet-connected devices, from phones to laptops to game consoles. All these devices have the potential for people to have access to games, and from an old age protection point of view, it is a major concern. One in five adults play games on a mobile phone, while those aged 16-34 are more likely to use gaming devices than average. Typically, 18-24 year olds will have a very small data footprint because they have little to no credit history. Machine learning can help identify minors who fall into this "thin file" category more easily, depending on other data points individuals may have. Behavioral surveillance may also play a role in age verification. A common problem, which many parents will experience, is children using their ID card to access services. With machine learning models and other tools, such as keyboard typing, it is possible to determine if the person is behaving as they would like. For example, if a teenager steals information from her 45-year-old mother, monitoring will indicate that they are not behaving as expected. Operators can then request additional verification checks to confirm your identity.
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Protect potential gambling addicts.
Protecting vulnerable customers is something that all responsible companies cannot ignore. According to our research, almost 9 million people use credit to cover the cost of living, while 1,8 million consider themselves in debt. If you then consider that 11.5 million adults have less than €100 in savings, leaving them with very little safety net to cope with an unforeseen change, it's clear that companies must embrace vulnerable customers who may be in financial distress and experience credit problems. affordability. Through the use of data tracking models, operators are able to see data about people's ability to purchase products and services on an ongoing basis, enabling them to make more responsible decisions about their customers with respect to what they offer.
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