The analytics start up firm BetBuddy and several researchers from the City University London developed new software that detects signs of player gambling addition at the early stages. This warning system detects problem gambling based on the playing patterns of individual players who have joined a self-exclusion program.
The funders of this research are Innovative UK under the Data Exploration program. The project is also backed by the Engineering and Physical Sciences Research Council, the RCUK Digital Economy Theme, the Defense Science and Technology Laboratory as well as by the Economic and Social Research Council.
According to the CEO at BetBuddy, Simo Dragicevic, the online gaming revenue in Europe on a yearly basis is expected to reach €13 billion, which is an amazing number, however, the industry has created about 593,000 players suffering from gambling addiction based on the figures from the National Health Service.
“By creating this software we give an example of how artificial intelligence and machine-based learning methods can be useful in addressing a very important social problem. Our project also addresses and promotes responsible gaming,” said Philip Nelson the chief executive at EPSRC.
The learning method of the software is known as fandom forests. Its creators say that it can achieve an accuracy of 87% in predicting gamers’ patterns which might lead to problem gambling. The software also determines whether or not to send promotional materials to players as well as whether to suggest self-exclusion.
“We want to help BetBuddy with their early warning system by testing and refining it in order to give providers an effective way of detecting signs of early gambling addiction as well as events that may show harm to players caused by gambling,” said Artur Garcez of City University London. “This system also enables players to use online gaming platforms more responsibly and securely,” he added.