Kindred partners with EASG to promote safer gambling
Kindred partners with the European Association for the Study of Gambling, EASG, to promote an app that can help identify early signs of gambling addiction to its customers.
The Bettor Time app has been developed by Zafty Intelligence, a leading company in mobile mental health assessment, using proprietary machine learning software to identify unique changes in user behaviour associated with mental health issues. The app will first be available to all Android customers with iOS to follow.
Kindred Group has committed to zero revenue from harmful gambling by 2023. Partnering with EASG and promoting the use of the Bettor Time app to customers will contribute towards achieving this target. Bettor Time is managed by the European Association for the Study of Gambling, EASG, who will control
and distribute the aggregated anonymised user data for academic research. The aim will be to share access to this app across all markets and the wider industry, ensuring that operators can offer their customers access to the app and improve their responsible gambling efforts based on any research conducted.
“For us promoting the Bettor Time App is a given and it is in line with our commitment of zero per cent revenue from harmful gambling by 2023. The technology behind this app with Zafty’s unique machine learning algorithms that take the recorded activity and learn each user’s normal behaviour is very interesting. This app can help users make better informed decisions about their gambling. We are proud to promote this app and I recommend other operators to follow”, says Maris Catania, Head of Responsible Gambling and Research, Kindred Group.
On the surface, the self-control app is essentially a time tracker that monitors a user’s time spent on gambling-related activities across all operators. To measure their use, the app timestamps recognised gambling products that are open and front of screen.
How does the app work?
The important work goes on in the background. Zafty’s unique machine learning algorithms take the recorded activity and learn each user’s normal behaviour. Once this baseline has been established, the system can monitor changes in activity, sometimes very subtle, that might indicate early signs of problem behaviour; for example, a player who regularly bets for two hours on a Saturday morning suddenly starting to play at midnight on a Tuesday.
Based on the level of change in the user’s behaviour, the app will recommend tailored features and support that will help the user to restore their gambling behaviour to a healthy level.