Deep technologies and safer gambling: a systematic review

Cardoso, LG, Barroso, BCR, Piccoli, G, Peixoto, M, Morgado, P, Marques, A, Rocha, C, Griffiths, MD ORCID: https://orcid.org/0000-0001-8880-6524, Queirós, R and Dores, AR, 2026. Deep technologies and safer gambling: a systematic review. Acta Psychologica, 262: 106140. ISSN 0001-6918

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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.

Item Type:	Journal article
Publication Title:	Acta Psychologica
Creators:	Cardoso, L.G., Barroso, B.C.R., Piccoli, G., Peixoto, M., Morgado, P., Marques, A., Rocha, C., Griffiths, M.D., Queirós, R. and Dores, A.R.
Publisher:	Elsevier BV
Date:	February 2026
Volume:	262
ISSN:	0001-6918
Identifiers:	Number Type 10.1016/j.actpsy.2025.106140 DOI 2553060 Other
Rights:	© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Divisions:	Schools > School of Social Sciences
Record created by:	Laura Borcherds
Date Added:	08 Jan 2026 16:31
Last Modified:	08 Jan 2026 16:31
URI:	https://irep.ntu.ac.uk/id/eprint/54988

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