BEHAVIORAL PATTERNS IN ONLINE GAMBLING IDENTIFIED THROUGH ARTIFICIAL INTELLIGENCE AND PSYCHIATRIC METHODS
Abstract
This study examined behavioral patterns in online gambling by using advanced technologies such as artificial intelligence (AI), machine learning, and the Internet of Behavior (IoB). The digital revolution has significantly increased access to online gambling, leading to the emergence of complex behavioral patterns among players. While many enjoy gambling as a form of recreation, the growing availability of online gambling poses a risks of addiction. The key role of AI and machine learning lies in the early detection of risky behavior among players. Algorithms can analyze gameplay data to identify patterns indicative of problematic behavior, including excessive spending and "chasing losses", which is the tendency to continue gambling or increase bets to recover losses. Appropriate interventions at the right time can mitigate the risk of developing gambling addiction. This research specifically focused on the use of machine learning and neural networks Multilayer Perceptron (MLP) to identify different player types, analyzing data from a group of online slot game players in the Republic of Srpska. Based on experience in clinical practice, models were trained on a sample of 200 players and tested on a broader group of 11,657 players to predict the risky behavior of players who played online slot games. Future research directions suggest the implementation of personalized tools for player control and support, with an emphasis on promoting responsible gambling and protecting public health. The results were evaluated using player data from the Republic of Srpska, a market governed by regulations designed to protect online gambling participants. This analysis underscored the role of regulatory frameworks and educational initiatives in mitigating the risk of gambling addiction.
References
American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Washington, DC: American Psychiatric Publishing; 2013. [CrossRef]
Auer M, Griffiths M. Predicting Limit-Setting Behavior of Gamblers Using Machine Learning Algorithms: A Real-World Study of Norwegian Gamblers Using Account Data. International Journal of Mental Health and Addiction 2022;20:1-18. [CrossRef]
Braverman J, Shaffer HJ. How do gamblers start gambling: identifying behavioural markers for high-risk internet gambling. Eur J Public Health 2012;22(2):273-8. [CrossRef] [PubMed]
Breiman L. Random forests. Mach Learn 2001;45(1):5-32. [CrossRef]
Čomić M, Knezevic V, Dickov A, Ratković D, Abazović M. Pathological gambling: Addiction or impulse control disorder? Timocki medicinski glasnik 2022; 47: 157-62. [CrossRef]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273-97. [CrossRef]
Cox DR. The regression analysis of binary sequences. J R Stat Soc Ser B 1958;20(2):215-32. [CrossRef]
Das S, Pandey MK. Behavioral Addictions: An Emerging Public Mental Health Crisis? Indian J Soc Psychiatry 2023;39(3):230-5. [CrossRef]
DataReportal. Digital in Bosnia and Herzegovina. [Internet]. 2024 [cited 2025 Jan 9]. Available from: https://datareportal.com/digital-in-bosnia-and-herzegovina.
Deng X, Lesch T, Clark L. Applying Data Science to Behavioral Analysis of Online Gambling. Curr Addict Rep 2019;6. [CrossRef]
Dragicevic SA, Garcez Cd, Percy C, Sarkar S. Understanding the Risk Profile of Gambling Behaviour through Machine Learning Predictive Modelling and Explanation. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019); 2019; Vancouver, Canada.
Finkenwirth S, MacDonald K, Deng X, Lesch T, Clark L. Using machine learning to predict self-exclusion status in online gamblers on the PlayNow.com platform in British Columbia. Int Gambl Stud 2020;21:1-18. [CrossRef]
Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen 1936;7(2):179-88. [CrossRef]
Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat. 2001;29(5):1189-232. [CrossRef]
Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn 2006;63(1):3-42. [CrossRef]
Hodgins DC, Stevens RMG. The impact of COVID-19 on gambling and gambling disorder: emerging data. Curr Opin Psychiatry 2021;34(4):332-43. [CrossRef] [PubMed]
Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970;12(1):55-67. [CrossRef]
IRBRS. Database of Economic indicators of RS. [Internet]. 2024 [cited 2025 Jan 9]. Available from: https://www.irbrs.net/statistika/UporedniPrikaz.aspx?tab=3&lang=eng.
Maron ME, Kuhns JL. On relevance, probabilistic indexing and information retrieval. J ACM. 1960;7(3):216-44. [CrossRef]
Percy C, Garcez Ad, Dragicevic S, Sarkar S. Lessons Learned from Problem Gambling Classification: Indirect Discrimination and Algorithmic Fairness. In: Proceedings of the CEUR Workshop; 2020; Virtual Symposium. Vol. 2884.
Republika Srpska Online. Gambling Statistics. [Internet]. 2024 [cited 2025 Jan 9]. Available from: https://www.republikasrpskaonline.com.
Salary Explorer. Salary Survey in Republika Srpska. [Internet]. 2024 [cited 2025 Jan 9]. Available from: https://www.salaryexplorer.com/salary-survey.php?loc=42&loctype=1.
Seo W, Kim N, Lee SK, Park SM. Machine learning-based analysis of adolescent gambling factors. J Behav Addict 2020;9:734-43. [CrossRef] [PubMed]
Slotegrator. Gambling in Bosnia and Herzegovina in 2022. [Internet]. 2022 [cited 2025 Jan 9]. Available from: https://slotegrator.pro/analytical_articles/gambling-in-bosnia-and-herzegovina-in-2022/.
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects: The 2022 Revision. [Internet]. 2024 [cited 2025 Jan 9]. Available from: https://population.un.org.
World Health Organization. International classification of diseases for mortality and morbidity statistics. 11th ed. [Internet]. 2019 [cited 2025 Jan 9]. Available from: https://icd.who.int/
World Population Review. Republika Srpska Population. [Internet]. 2024 [cited 2025 Jan 9]. Available from: https://worldpopulationreview.com/countries/republic-of-srpska-population.
World Population Review. Republika Srpska Population. [Internet]. 2024 [cited 2025 Jan 9]. Available from: https://worldpopulationreview.com/countries/republic-of-srpska-population.
