Problem gambling has long been one of the most pressing issues in the gaming world. While the vast majority of gamblers have no problem playing responsibly, the small percentage who do develop compulsive gambling problems can cause significant harm to themselves, their loved ones, and society as a whole.
Who Wants to Know?
That’s why being able to find and treat problem gamblers before their addictions get out of control has been an important goal for researchers and some industry officials alike. Now, a collection of scientists and gambling consultants say they may be able to better diagnose high-risk gamblers simply by looking at the data they generate while they play.
At the moment, the best way to diagnose a gambling problem is through the use of questionnaires and interviews by qualified therapists. The data-mining approach isn’t one that can replace this method, but it could be an important way to find those players who are at serious risk of gambling problems early on, and steer them towards getting help before it’s too late.
The early forms of these systems use algorithms and computerized models that are based on customer-tracking information, such as the information gathered by online casinos or frequent-player cards at brick-and-mortar locations. For instance, Harvard Medical School professor Sarah Nelson showed off one such mathematical algorithm at a conference at Caesars Palace in Las Vegas, one that was designed to track the habits of sports bettors. It took into account how often an individual made bets, the size of their bets, and more. Some algorithms are said to take as many as 800 variables into account.
Already, some of these systems are being used at government-run casinos around the world, and some online gambling sites have also decided to utilize these programs. Every system is unique, but they all work to find signs of potential problems (perhaps including loss chasing, unusually long sessions, or major changes in an individual’s playing behavior) so that players who may be at risk of becoming compulsive gamblers can be identified. Players can then be given information or prevented from playing, depending on the policy of the casino or site.
Too Much Information
While these data mining programs have proven popular among those around the industry, casino executives themselves have been less enthusiastic about them.
“I think it’s a terrible idea,” Caesars Entertainment CEO Gary Loveman told the Wall Street Journal. “Is it McDonald’s obligation to decide you have a problem because you have a tendency to eat high-calorie lunches? You could take this to ridiculous extremes.”
Casinos may have other motives for opposing the use of algorithms that might find not only that some of their biggest clients have gambling problems, but that these issues can be found early. While casino executives maintain that problem gambling doesn’t make up a large percentage of their business, some studies have suggested otherwise.
For instance, one Harvard study found that customers at an unnamed online gambling site who triggered a “responsible gaming alert” lost as much as 12 times more on average than a random sampling of customers. Meanwhile, an Australian government study found that at one club, 2.3% of loyalty-card holders were responsible for 76% of all losses – and suggested that overall, 41% of slots losses came from problem gamblers.