Designing an expert system for fraud detection in private telecommunications networks
Telecommunications fraud not only burdens telecom providers accountings but burdens individual users as well. The latter are particularly affected in the case of superimposed fraud where the fraudster uses a legitimate users account in parallel with the user. These cases are usually identified after user complaints for excess billing. However, inside the network of a large firm or organization, superimposed fraud may go undetected for some time. The present paper deals with the detection of fraudulent telecom activity inside large organizations premises. Focus is given on superimposed fraud detection. The problem is attacked via the construction of an expert system which incorporates both the network administrators expert knowledge and knowledge derived from the application of data mining techniques on real world data.
expert systems fraud detection telecommunications user modeling data mining applications