and stored at:ĭirect URL to data: doi: 10.17632/9j5gcygnwg.1 Secondary data was sourced from a gambling payments provider located in the U.S. The datasets provided herein are 1-year subsets of the transaction log file March 1st, 2019 through to February 29th, 2020. Data for each transaction are recorded and associated with a unique identifier for each customer. Raw transactional data from all customer accounts are collected and stored by the payment provider in a transaction log file. Subsets of the data were retrieved using this architecture and converted to CSV format. The data were stored on a compute cluster using Hadoop Distributed File System and Apache Spark. Raw data were obtained from a gambling payments provider based in the U.S.

The granularity and timespan of the data provide an opportunity to apply a variety of data science and machine learning techniques.īusiness, Management and decision sciences With digital payments becoming increasingly prevalent across the gambling industry, these data can be used to explore how individuals’ payment behavior can inform us about their gambling behavior. These data will be particularly helpful to researchers in the fields of gambling studies and behavioral sciences, and more generally for data and computer scientists. The subsets contain 1-year of customer payments transaction records for two gambling merchants: (1) a casino-focused brand and (2) a sports-focused brand.

With this article we describe the transaction log file and provide two filtered subsets of the data. One of these raw datasets is a transaction log file representing customer payment transaction data across a variety of gambling merchants (e.g., online casinos, sportsbooks, and lottery providers). The raw datasets cover a time period of 6-years (2015-2021), representing over 300,000 customers and approximately 90 million transaction records. based provider of digital gambling payments systems, who has demanded to remain anonymous.

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