Online and mobile gaming operators are working in a market which is changing at a rapid pace. With the growth of new immersive technologies such as augmented and virtual reality, this rapid change of pace is placing intense competition on operators to attract
and keep customers. Ongoing innovation is key to success.
But in such an environment, new issues will arise and new solutions will need to be found. Thanks primarily to the proliferation of internet-connected devices, operators need to be aware of how an abundance of accessibility can cause potential risks for
In two areas specifically, Experian believes machine learning can help. Namely, the protection of minors and the identification of problem gamblers.
As most parents will be aware, teenagers nowadays have access to a host of internet connected devices, whether that’s their phone, laptop, or games console. All these devices have the potential for individuals to access games, and from an age protection
point of view, this is a substantial concern. One in five adults play games on a mobile, while those aged between 16-34 are more likely than average to use devices for gaming as well.
Typically, 18-24 year olds will have a very small data footprint, as they have little or no credit history. Machine learning can help identify minors who have fall into this ‘thin file’ category more readily, based on other alternative data points individuals
Behavioural monitoring can also play a part in age verification. A common issue which many parents will be familiar with, is children using their ID, to gain access to services.
Thanks to machine learning models, and other tools – such as keystroke analysis – it is becoming possible to determine if the individual is behaving like you would expect those in their peer group would do.
For example, if a teenager steals their 45-year-old mother’s information, monitoring will be able to flag that they’re not behaving in a way like you’d expect. Then operators can ask for additional verification checks, to confirm their identity.
Protecting vulnerable customers is something any responsible businesses can’t ignore. According to Experian research, nearly 9million people are using credit to cover the cost of everyday living expenses, while 1.8million are defined as being in persistent
If you then consider that 11.5million adults have less than £100 in savings – leaving them with very little in terms of a safety net to deal with an unexpected change in their circumstances – it’s clear that businesses need to account for vulnerable customers
who could be financially distressed and experience affordability issues.
Using trended data models, operators can see data on the ability of individuals to afford products and services on an ongoing basis, giving them the opportunity to make more responsible decisions over their customers in terms of what they offer customers.
With gaming now more ubiquitous and accessible than ever before, machine learning can provide greater assurance for both operators and customers alike, and steps are already being made.
In the modern world, consumers are becoming increasingly conscientious about the type of businesses they deal with, expecting them to operate with ethical considerations at the heart of their decisions. Furthermore, it’s likely calls to implement greater
verification checks will grow, if the industry comes under closer scrutiny from both regulators and the Government.
Operators can get ahead by embracing machine learning, ensuring they are fully prepared, if and when, any new possible regulation comes into force, while showing that they are doing what they can to act in a responsible manner.
Those that don’t risk their reputation being damaged, and falling behind their competitors that have been willing and able to exploit the benefits machine learning can bring.
External | what does this mean?