Gambling safety net : Predicting the risk of problem gambling using Bayesian networks

• Main Author: Sikiric, Kristian
• Format: Others
• Language: English
• Published: Linköpings universitet, Databas och informationsteknik 2020
• Subjects: Machine learning; Bayesian networks; problem gambling; Computer Engineering; Datorteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165867

As online casino and betting increases in popularity across the globe, the importance of green gambling has become an important subject of discussion. The Swedish betting company, ATG, realises the benefits of this and would like to prevent their gamblers from falling into problem gambling. To predict problem gambling, Bayesian networks were trained on previously identified problem gamblers, separated into seven risk groups. The network was then able to predict the risk group of previously unseen gamblers with an ac- curacy of 94%. It also achieved an average precision of 89%, an average recall of 96% and an average f1-score of 93%. The features in the data set were also ranked, to find which were most important in predicting problem gambling. It was found that municipality, which day of the week the transaction was made and during which hour of the day were the most important features. Also, the Bayesian network was also made as simple as possible, by removing irrelevant features and features which carry very low importance.