Leveraging predictive modeling to reduce signal theft in a multi-service organization environment
Seymour Douglas · 2012
Signal theft can be defined as the interdiction, consumption or usage of carrier signal from a provider's network without payment or payment of an amount less than the level of service consumed. High levels of signal theft can potentially reflect open technical network issues, failure of electronic countermeasures or operational gaps that are estimated to cost the cable industry providers more than $5 billion annually. This session will discuss the business challenges associated with the quantification of signal theft-related losses, outline some of the countermeasures taken by MSOs, and then provide views on the development of predictive models to help identify the potential likelihood of signal theft in a given environment. We will examine the performance of certain machine learning algorithms as well as data challenges associated with both the architecture construction and analytical efforts, and conclude with a lessons-learned discussion and views on future approaches.