Fraud Detection

The ZIM project "Entwicklung eines verteilten, zweistufigen Fraud-Detection System basierend auf künstlicher Intelligenz sowie Implementierung dieses Systems auf ARM-basierenden Hybridservern; Entwicklung einer Selektionsmethodik zur Detektion von Fraudmustern auf Basis von Stream Based Active Learning mit Konfidenz > 95% und Entwicklung einer verteilten Serverstruktur (Hybridcloud und Hybridservern) auf ARM-Basis für Echtzeitanalyse" investigates the problem of fraud detection. Machine Learning and Stream Based Active Learning are used to detect fraudulent behavior targetting telephone provider, telephone exchange and their customers. The recorded data is handled as a data stream to be able to do real time analysis using the call detail record. Stream Based Active Learning is used to adapt known fraud patterns to the fraudsters' changing behavior as well as finding new patterns. The learned classifier has to be interpretable, which means that the decisions has to be understandable, to be able to explain the decisions in case of a dispute.