The Communications Fraud Control Association estimates that fraud costs telecommunications providers and their customers more than $12 billion annually. This staggering figure implies that the Telecom industry is in urgent need to adopt advanced analytic techniques in order to protect losses, and to avoid passing along these potentially avoidable costs to consumers. The purpose of this article is two-fold. Firstly, I present the traditional analytical approaches of acquisition modeling, and retention modeling. However, these approaches are not strong enough for the necessary data mining that would uncover undetected fraud-predictive relationships. This knowledge can be used for predictive modeling with the above mentioned techniques. Thus, secondly, I apply the data mining muscle, and the alongst predictive power of the new machine learning method – the GenIQ Model© – for Telecom fraud reduction.