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AI algorithms require a training dataset to learn the patterns of a prespecified group. Problem gambling screens are one method for the collection of the necessary input data to train AI algorithms. The present study\u2019s main aim was to identify the most significant behavioral patterns which predict self-reported problem gambling. In order to fulfil the aim, the study analyzed data from a sample of real-world online casino players and matched their self-report (subjective) responses concerning problem gambling with the participants\u2019 actual (objective) gambling behavior. More specifically, the authors were given access to the raw data of 1,287 players from a European online gambling casino who answered questions on the Problem Gambling Severity Index (PGSI) between September 2021 and February 2022. Random forest and gradient boost machine algorithms were trained to predict self-reported problem gambling based on the independent variables (e.g., wagering, depositing, gambling frequency). The random forest model predicted self-reported problem gambling better than gradient boost. Moreover, problem gamblers showed a distinct pattern with respect to their gambling based on the player tracking data. More specifically, problem gamblers lost more money per gambling day, lost more money per gambling session, and deposited money more frequently per gambling session. Problem gamblers also tended to deplete their gambling accounts more frequently compared to non-problem gamblers. A subgroup of problem gamblers identified as being at greater harm (based on their response to PGSI items) showed even higher values with respect to the aforementioned gambling behaviors. The study showed that self-reported problem gambling can be predicted by AI algorithms with high accuracy based on player tracking data.<\/jats:p>","DOI":"10.1007\/s10899-022-10139-1","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T11:06:52Z","timestamp":1658228812000},"page":"1273-1294","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting"],"prefix":"10.1007","volume":"39","author":[{"given":"Michael","family":"Auer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8880-6524","authenticated-orcid":false,"given":"Mark D.","family":"Griffiths","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"issue":"4","key":"10139_CR1","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1111\/add.14083","volume":"113","author":"M Abbott","year":"2018","unstructured":"Abbott, M., Romild, U., & Volberg, R. 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