A hybrid fraud scoring and spike detection technique in streaming data
The aim has been to propose a fraud detection system with capabilities of minimizing false alarms. In this paper we introduce a technique, which uses a hybrid fraud scoring and spike detection technique in streaming data over time and space. The technique itself differentiates normal, fraud and anomalous links, and increases the suspicion of fraud links with a dynamic global black list. Also, it mitigates the suspicion of normal links with a dynamic global white list. In addition, this technique uses spike detection technique to highlight the sudden and sharp rises in data, which can be indicative of abuse. The purpose is to derive two accurate suspicion scores for all incoming new examples in real-time. Results on mining several thousand credit application data demonstrate that the proposed technique reduces false alarm rates while maintaining a reasonable hit rate. In addition, new insights have been observed from the relationships between examples. The proposed technique takes the advantages of anomaly detection and supervised techniques. However by employing the spike detection technique, the false alarm rate is decreased. By this novel integration of techniques, the proposed technique is able to foil fraudsters' attempts, which continuously morph their styles to avoid to be detected. The results of the experiments to demonstrate the benefits of the technique are also presented in this paper.
spike detection streaming data fraud detection anomaly detection