Deep technologies and safer gambling: A systematic review

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Authors: Leonor G. Cardoso, Beatriz Barroso, Gloria Piccoli, Miguel Peixoto, Pedro Morgado, António Marques, Carla Rocha, Mark D. Griffiths, Ricardo Queirós, Artemisa R. Dores
Cited by: 2
References: 104

Abstract

Deep technologies combine engineering innovation and scientific findings to solve complex problems and are becoming particularly relevant to the gambling industry. With the global rise of gambling practices and the subsequent increase of gambling-related problems and disorders, deep technologies have emerged as a way to create safer online gambling environments. However, there is still limited knowledge regarding their applicability and consequences. The present study systematically reviewed the existing literature on deep technologies in gambling environments, such as online casinos and betting platforms, and explored their potential benefits, risks, and effectiveness in promoting safer gambling experiences. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Searches were conducted in Web of Science, PubMed, Scopus, EBSCO, and IEEE databases, and manually. A total of sixty-eight studies were included in the review. In general, four primary applications of deep technologies in online settings were found: (i) behavioural monitoring and feedback; (ii) predictive risk modelling; (iii) decision support and AI classifiers; and (iv) limit-setting/self-exclusion tools. They were primarily used to identify and classify problematic gambling, prompt individual action, regulate gambling behaviours, raise awareness of risk levels, promote responsible gambling practices, support research, interventions, and evaluate player protection initiatives. Together, the findings suggest that deep technologies offer ample opportunities to enhance gambler safety and reduce potential risks, although challenges may arise from their implementation, such as privacy and ethical concerns, malicious data use, misclassification of risk levels, and difficulties in large-scale application. Limitations and directions for future studies are discussed. • Deep tech enables the application of data-informed responsible gambling strategies • Data tracking allows real-time monitoring, pattern identification and feedback • Advanced models are effective in early detection and prediction of problem gambling • Timely prevention and harm minimization strategies tailored to the user • Stronger regulations are crucial to safely implement deep tech in gambling

Fields of Study & Subjects

Gambling Behavior and Treatments Artificial Intelligence in Healthcare and Education Advanced Bandit Algorithms Research

Publication details

Journal / source
Acta Psychologica
Publisher
Elsevier BV
Volume
262
Pages
106140–106140
Publication date
6 January 2026
Language
EN