The gaming industry is rapidly evolving these days. Having moved to mobile and online platforms, casino providers barely keep pace with implementing another pack of cutting-edge technologies, including virtual and augmented reality. All of this makes already intense competition even harder. There’s no doubt, ongoing innovation is a key to attracting huge crowds of customers.
However, such a rapidly-changing environment generates a number of new issues. In particular, a mind-blowing abundance of accessibility creates a slew of problems that need to be adequately handled. That’s the case where machine learning comes in handy. For example, it can help to protect minors and fight game addiction.
Today, teens are able to access the World Wide Web literally from anywhere, including not only their laptops and smartphones but even game consoles.
As a rule, teens have rather a small data footprint due to the lack of credit history. With machine learning, it won’t be a problem to spot teens falling into this specific category because in this case other alternative data points will be utilized for identification.
Behavioral monitoring matters too when it comes to age verification. A common issue faced by many parents is that their kids make use of their ID to access the required services.
If a teen thieves his 45-year-old mom’s data to play the best canadian slots, with the help of monitoring one can learn that he’s not behaving in the expected way. After this, operators can request extra verification checks with the aim of confirming the teen’s identity.
Taking care of gambling addicts
Protecting vulnerable clients is what any responsible company can’t neglect. As studies revealed, almost 9 million folks are making use of credit just to cover the cost of daily living expenses, and nearly 1.8 million are heavily in debt all the time.
Up to 11.5 million adults’ savings don’t even exceed £100. It’s clear that it makes them especially vulnerable in terms of a safety net. So, a necessity arises for businesses to take care of their vulnerable clients who could face affordability issues and be financially distressed.
With the help of trended data models, operators can closely watch data on the ability of customers to afford services and products on a regular basis. It will help them to make wise decisions over their clients.