Abstract
Review of Fraud Detection and Churn Behavior Modeling Techniques
Highlights
Since the beginning of commercial telecommunications, the fraudsters have been causing financial damage to the companies who offered these services[1]
There is need for further research work on, “Enhanced Predictive Data Mining Algorithms for Fraud Detection and Churn Behavior Modeling in Telecommunication Systems” so as to bridges a gap in knowledge by introducing data mining as an advanced machine learning approach which is applicable in Customer relationship management realm
After the significant studies regarding the customer churn from both descriptive and predictive point of view were reviewed, the issue of data imbalance in churn datasets was discussed and remedies for it were extracted from the previous studies
Summary
This research pursues inductive/deductive approach by studying existing techniques for fraud detection and customer churn prediction. Telecommunication operators for instance store large amounts of data related with the activity of their clients. In these records exists both normal and fraudulent activity records. It is expected that the fraudulent activity records should be substantially smaller than the normal activity. Among all industries which suffer from this issue, telecommunications industry can be considered in the top of the list with approximate annual churn rate of 30%. This means wasting the money and efforts, “it is like adding water to a leaking bucket”. Abbreviation: DT: Decision Trees; LR: Logistic Regression; NN: Neural Networks; NBC: Naive Bayesian Classifiers; SVM: Support Vector Machine; LDA: Linear Discriminant Analysis; SOM: Self-Organizing Maps
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