An Application of Decision Trees for Rule Extraction Towards Telecommunications Fraud Detection

Telecommunications fraud has drawn the attention in research due to the huge economic burden on companies and to the interesting aspect of users' behavior modeling. In the present paper, an application of decision trees to fraud detection is presented. The appropriate user behavior modeling is, also, discussed. The trees are used for rule extraction in order to distinguish between normal and fraudulent activities in a telecommunications network. Several real cases of defrauded user accounts are modeled by means of selected usage features. Decision trees are applied in order to identify the critical values that separate fraud from legal use. These thresholds are expressed in the form of rules that will be used in a rule based expert system.

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