Using Data-Driven and Process Mining Techniques for Identifying and Characterizing Problem Gamblers in New Zealand

Suriadi Suriadi, Teo Susnjak, Agate M. Ponder-Sutton, Paul A. Watters, Christoph Schumacher


This article uses data-driven techniques combined with established theory in order to analyse gambling behavioural patterns of 91 thousand individuals on a real-world fixed-odds gambling dataset in New Zealand. This research uniquely integrates a mixture of process mining, data mining and confirmatory statistical techniques in order to categorise different sub-groups of gamblers, with the explicit motivation of identifying problem gambling behaviours and reporting on the challenges and lessons learned from our case study.

We demonstrate how techniques from various disciplines can be combined in order to gain insight into the behavioural patterns exhibited by different types of gamblers, as well as provide assurances of the correctness of our approach and findings. A highlight of this case study is both the methodology which demonstrates how such a combination of techniques provides a rich set of effective tools to undertake an exploratory and open-ended data analysis project that is guided by the process cube concept, as well as the findings themselves which indicate that the contribution that problem gamblers make to the total volume, expenditure, and revenue is higher than previous studies have maintained.


Data mining; process mining; confirmatory statistics; problem gambling

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DOI: 10.7250/csimq.2016-9.03


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