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Our study focused on a use case of a small developing island with a complex telecommunication infrastructure, but with constraints regarding fraud monitoring/management resources. Using historical premium rate calls data, we firstly identify the most influential parameters for detecting fraud by performing feature selection through considering the call variablesĄ¯ information gain and pairwise correlation. WardĄ¯s Hierarchical Divisive Clustering algorithm is then applied to the data using the selected features. Different from the related works, a traditional accuracy-based Classification and Regression Tree approach is adopted for classifying PRS calls, and compared to its cost-sensitive counterpart. The research aims to highlight the advantage of using cost-based analyses in the telecommunication industry, particularly as it relates to reducing revenue leakage associated with premium rate services fraud. |
IEEE/ICACT20220012 Slide.08
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