A team of researchers and analytic firm have created an early warning system that notifies gamblers who show signs of addiction at an early stage.

Software analytics start-up BetBuddy has collaborated with researchers from City University London to develop a software that determines signs of risk or addiction based on gambling patterns of individuals who voluntarily join a self-exclusion program.

The software’s learning method known as “random forests” could achieve 87% accuracy in predicting playing patterns which were likely to lead in gambling addiction. The system also determines whether or not to send users marketing materials, or whether to suggest self-exclusion to the player.

The research was funded by Innovate UK, under its Data Exploration program and is backed by the RCUK Digital Economy Theme, the Engineering and Physical Sciences Research Council (EPSRC), the Economic and Social Research Council (ESRC) and the Defense Science and Technology Laboratory (DSTL).