The system pinpoints whether a player’s gambling patterns are exhibiting signs of risk and starting to match those of previous players who asked online gambling sites to block them, for a fixed period, to stop them becoming ‘hooked’ -- an option known as ‘self-exclusion’.
The researchers found that by harnessing a machine learning method known as ‘random forests’ and applying it to a real-world online gambling dataset, the system could achieve 87 percent accuracy in predicting playing patterns which were likely to evolve in an unhealthy direction.
“This project is an example of how artificial intelligence and machine learning methods can be used to address an important social problem,” said professor Philip Nelson, chief executive, Engineering and Physical Sciences Research Council (EPSRC) in Britain that supported the research. The researchers from City University London has worked with software analytics company BetBuddy to enhance the accuracy of the computer models underpinning the system according to the very latest understanding of the psychological pathways to gambling addiction.
“All UK gambling providers are legally obliged to offer customers a self-exclusion option,” Artur Garcez of City University London said.
“Our aim has been to help BetBuddy test and refine their system so that it gives providers an effective way of predicting at an earlier stage self-exclusion as well as other signals or events that indicate harm in gambling. This enables customers to use online gambling platforms more securely and responsibly,” Garcez pointed out in a statement released by EPSRC on Friday.