Abstract
The use of data analytics methods for behavioral analysis of gamblers has been of interest in the gambling field. Most of the research on this topic has been conducted using self-reported survey data due to the limited availability of quantitative data such as behavioral tracking data. To fill in this gap, we describe a dataset comprising financial payments records for modeling behavioral patterns of gamblers using quantifiable variables. This data has been obtained from a digital payments provider, which acts as an intermediary between customers’ banks and gambling merchants. In this paper, we provide a descriptive analysis of this data comprising its distribution with respect to transaction volume and amounts, outlier analysis, auto-correlation analysis, and stationarity analysis. From this analysis, we conclude that this data is right skewed with the largest number of transactions taking place after 2019. We also conclude that the data is non-stationary and does not exhibit any significant auto-regressive characteristics. Stationarity and seasonality for this data will need to be addressed for applying statistical time-series forecasting models. It is worth noting that this data is limited to customers in the USA and only includes details on money committed to gambling and not detailed betting behavior. It also does not take account other methods of payments available to customers and the possibility of customers having multiple accounts with the same payments provider. Additionally, since merchant IDs and customer IDs have been obfuscated, further analysis on merchants and specific customers could be impacted.
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Puranik, P., Taghva, K., Ghaharian, K. (2023). Descriptive Analysis of Gambling Data for Data Mining of Behavioral Patterns. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_4
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DOI: https://doi.org/10.1007/978-3-031-35308-6_4
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