Submission Type

Presentation

Submission Title

Exploring new methods of harm analysis with Artificial Intelligence: Application of Recurrent Neural Networks, Clustering and Supervised Learning to classify behavioural events and anomalies in real-time

Session Title

Session 1-4-B: Technology and Harm Minimization

Location

Caesars Palace, Las Vegas, Nevada

Start Date

28-5-2019 3:30 PM

End Date

28-5-2019 4:55 PM

Disciplines

Artificial Intelligence and Robotics

Abstract

Traditional approaches in the application of machine learning to detect harm have used supervised machine learning techniques to train models using historic player data. These techniques typically prove most useful when a reasonable amount of historic player data is used to define model features. Whilst some of these models have proved effective at predicting events such as long-term self-exclusion, they often miss a large proportion of at-risk players who close their accounts or self-exclude soon after registration and who experience episodic bursts of intensive activity that can lead to harm.

In an effort to improve the state-of-the-art, BetBuddy and City, University of London, applied a variety of artificial intelligence techniques, including deep learning, to a UK online casino real money gambling data set to undertake i) streaming data analysis to predict events and/or flag anomalies, ii) session analysis as players approach self-exclusion, and iii) very early prediction following registration.

The research results have proved promising. Predicting self-exclusion using ‘day 1’ data, that includes a on site activity and demographic data, provided competitive model performance when benchmarked to PwC / GambleAware PGSI research. The application of recurrent neural networks was able to demonstrate the ability to predict real-time events and anomalies.

Keywords

online casino, recurrent neural network, supervised learning, artificial intelligence, problem gambling, anomaly detection

Author Bio

Simo is CEO of BetBuddy, Playtech’s responsible gambling data analytics company. Simo is an expert in consumer behaviour analysis and consumer protection and has published research in leading journals and conferences, including ECAI, NIPS, the Journal of Gambling Studies, and International Gambling Studies. Simo is a supervisor on the Ph.D programme at the Research Centre for Machine Learning at City, University of London, and was named AMBA Entrepreneur of the Year in 2013.

Funding Sources

The research was funded by Playtech Plc. The research design adn execution was overseen by BetBuddy (part of Playtech Plc) and City, University of London. The research was undertaken by three MSc student interns and overseen by Simo Dragicevic (BetBuddy) and Professor Garcez, Dr Weyde, and Dr Turkay (all City, University of London).

Competing Interests

Simo Dragicevic is employed by BetBuddy, a wholly owned subsidiary of Playtech Plc, a significant industry supplier and operator. Simo is also an External supervisor at City, University of London Current and previous research projects funded by Kindred Group, InnovateUK, EPSRC, ESRC and DSTL.

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May 28th, 3:30 PM May 28th, 4:55 PM

Exploring new methods of harm analysis with Artificial Intelligence: Application of Recurrent Neural Networks, Clustering and Supervised Learning to classify behavioural events and anomalies in real-time

Caesars Palace, Las Vegas, Nevada

Traditional approaches in the application of machine learning to detect harm have used supervised machine learning techniques to train models using historic player data. These techniques typically prove most useful when a reasonable amount of historic player data is used to define model features. Whilst some of these models have proved effective at predicting events such as long-term self-exclusion, they often miss a large proportion of at-risk players who close their accounts or self-exclude soon after registration and who experience episodic bursts of intensive activity that can lead to harm.

In an effort to improve the state-of-the-art, BetBuddy and City, University of London, applied a variety of artificial intelligence techniques, including deep learning, to a UK online casino real money gambling data set to undertake i) streaming data analysis to predict events and/or flag anomalies, ii) session analysis as players approach self-exclusion, and iii) very early prediction following registration.

The research results have proved promising. Predicting self-exclusion using ‘day 1’ data, that includes a on site activity and demographic data, provided competitive model performance when benchmarked to PwC / GambleAware PGSI research. The application of recurrent neural networks was able to demonstrate the ability to predict real-time events and anomalies.