Abstract
This paper 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.
Highlights
IntroductionPervasive use of digital technologies has left rich digital traces about the physical objects of our world (e.g. purchase orders, sales figures) and about our activities and our behaviours
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
It does not offer any insights into the behaviour of gamblers seen in each cluster. We contend that this could be a contributing factor to the experience found in other similar studies, e.g. [14] which found the use of k-means clustering in their analysis to be rather ineffective. We demonstrate how both process mining analysis, with its proven ability to extract detailed behavioural insights from fine-grained and chronologically-arranged data, as evidenced by the insights obtained from previous process mining case studies [7], [8], [9], [15], and confirmatory statistics, can be weaved together with clustering analysis to understand the variety of behaviours exhibited by gamblers, and to evaluate the results
Summary
Pervasive use of digital technologies has left rich digital traces about the physical objects of our world (e.g. purchase orders, sales figures) and about our activities and our behaviours. The increased availability of detailed event logs (due to the widespread use of process-aware information systems) coupled with maturing process mining techniques [6] have recently enabled wider applications of process mining in organisations around the world, such as in a large Australian insurance organisation [7] and others [8], [9], [10], [11] This article reports on the techniques applied, challenges and lessons learned from our case study where a mixture of process mining, data mining, and confirmatory statistical techniques are applied to analyse a data set containing information about all fixed-odds bets (FOBs) recorded by a gambling service provider in New Zealand during a timeframe between 2013-2014. A discussion about the related work is provided in Section 5, followed by the conclusion
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