Kindred Group reviews machine learning to address problem gambling

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One online gaming business is looking into how AI and machine learning might help tackle gambling addiction

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Unibet parent company Kindred Group is turning to machine learning to recognise patterns of problem gambling across its suite of betting and casino websites.

Kindred Futures is the group's innovation wing, tasked with bringing in entrepreneurs and experts on how to "co-create the future of gambling," according to the organisation's website. It recently held a working group that brought in academics and figures from the technology industry to discuss how machine learning and AI might be able to help reduce problem gambling.

According to statistics from the Gambling Commission published by NHS Digital in 2012, 62 percent of all people gambled in that year, 0.5 percent of people identified as problem gamblers, and 4.3 percent of people were described as being “at low or moderate risk of developing problems with their gambling”. A combined report is scheduled to be published in spring this year.

Kindred Group already has a proprietary system in place – Player Safety Early Detection System, or PS-EDS – designed to raise red flags if players are thought to be at risk. The head of Kindred Futures, Will Mace, describes the existing system as "industry-leading" but also "rudimentary".

"It's a set of rules to do with behaviour – chasing losses, chasing wins, significant changes in your deposit pattern, that sort of thing," Mace explains. "But there can be loads of explanations for those – maybe you bet a couple of quid here and there, then all of a sudden you start betting hundreds of pounds. This would probably raise a flag, and we'd look to see if we can understand what's going on."

At present, the responsible gambling department make joint decisions on when they should intervene. Players aren't aware of the system until somebody contacts them.

"We record all transactional behaviour on our sites for obvious regulatory and operational reasons so when those things coincide and hit a rule we decide what to do about it," he says. "[But] if you were playing on our sites you wouldn't know anything that was happening, and then when a flag was raised you wouldn't know a flag was raised until we decided it was the right thing to do to get in touch with you."

Mace hopes that a machine learning-led approach that combines different data sets could augment the existing platform and even help automate the process to some degree.

A problem is that there are no set guidelines in place – everyone's circumstance is different, their habits will be different, and if they were really set on circumventing risk-reducing measures they could simply go to another website or walk into a bricks-and-mortar betting shop.

"User behaviour takes many forms – the interesting thing in this particular case is there's no single definition of problem gambling," Mace says. "What's a gambling pattern to me might be a problem but to you it might just be your normal form of entertainment. There is no 'if you do X and Y' you have a problem, everyone is different – that's why the more rudimentary methods have their shortcomings."

It's early days for Kindred Group, and Mace does not yet know what the platform will look like or if a machine learning integration will be successful. But the company is now in discussion with one of the startups it invited in for talks and is in the design phase.

Addressing problematic behaviour for online gambling is something the gaming companies will have to pay closer attention to whether they like it or not due to regulations from Europe, according to Mace.

"The regulators are increasingly insistent upon companies having the right, responsible gambling strategies, attitudes and approaches in order to both obtain and maintain a licence," Mace explains. "That's relatively new."

But for a truly effective approach to combating problem gambling, there would need to be some degree of collaboration across the betting websites. This might look like regulators pushing the operators to run a cross-website semi-anonymised way to pool their knowledge.

"It's definitely a challenge to an individual company like us trying to detect problem behaviour when you can just as easily bet with someone else," says Mace. "There's all sorts of challenges that we're taking steps towards seeing what we can do, and hopefully developing momentum from the industry to come together on it.

"Until there's collaboration across those sites nobody's going to have a complete picture of your betting behaviour, and therefore a lot of people who do have a problem are going to go unnoticed even if you bring in non-betting behaviour sources."

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