# Time Series Clustering of Online Gambling Activities for Addicted Users' Detection
## 2 Literature Review  
In response to the proliferation of online gambling services, the scientific literature has proposed the use of AI-based techniques to analyze this phenomenon. Broadly speaking, two primary strands of research are identifiable in the literature. The first of these threads aims to understand the behavior of gamblers, to undertake business policies capable of effectively retaining them as consumers. To that end, churn prediction has proven to be a promising tactical option within the context of the Customer Relationship Management (CRM) strategy for analyzing the dynamics and dimensions of customer retention. In this context, it refers to the process of identifying gamblers with a high probability of leaving the company, on the basis of past behavior. For instance, Reference [6] investigated whether churn prediction is a valuable option to include in the CRM palette of online gambling companies. Using real-life data provided by an online gambling company, a combination of single algorithms, CART decision trees, and generalized additive models is benchmarked to their ensemble counterparts. The results reflect the strategic value of churn prediction for identifying and profiling those customers that, with a high degree of probability, might leave the gambling provider, consequently causing a loss of revenue. The second strand of research aims to study the characteristic behaviors of so-called gambling addicts. The ability to identify gamblers whose behavior resembles that of gamblers who elected to use self-exclusion tools or were identified as problematic gamblers by other online platforms could, for instance, be used to target responsible gaming messages to amplify self-awareness among gamblers, thereby reducing the risk of adverse outcomes [7]. Depending on the availability of transactional gambling data, most research aimed at identifying the predictors for gambling-related problems focuses on daily aggregates of gambling transactions from business-to-customer gambling operations [8]. In spite of the burgeoning interest in this area, the vast majority of existing studies do not use AI-based techniques to aid in the detection of addicted gamblers; instead, they rely on simple statistical techniques to accomplish the same objectives. In a recent study [8], the authors examined all peer-reviewed empirical evidence underpinning the pursuit of Responsible Gambling (RG) strategies. The results show that current evidence about RG initiatives and programs is very limited. The authors observed the nascent state of the field, which has not yet progressed to best practices that are supported by scientific evidence, and further, to the fact that RG programs that rely on AI tools remain largely at the nascent stage of development. A recent contribution to the literature [9] used Artificial Neural Networks (ANNs) to investigate the extent to which specific payment behaviors and methods facilitate accurate identification of problematic gamblers; the point of origin for the study is the transactional data generated by customers of the online gambling provider bwin.com. The resulting ANN is quite naive and incapable of capturing the intrinsic temporal dimension of the data. Likewise, the authors of [10] evaluated a set of classification and regression algorithms to determine which techniques are more qualified to identify probable addicted gamblers. According to this study, while ANNs are evidently the most reliable classification method overall, they still fail to identify a meaningfully large group of likely problem gamblers. Such studies indicate that the use of AI techniques to detect gambling addicts is still in its infancy. Scientific literature [11] has pointed out that, to understand gambling behavior, it is essential to examine the behavioral patterns of play; there is good evidence to suggest that past behaviors can be used to predict future gambling actions and potential problems.