Multilayered Approach for Identity Crime Detection system

Identity crime is well known, prevalent, and costly, and credit application scam is a specific case of identity crime. The methods communal detection (CD) and spike detection (SD) are unsupervised algorithms. CD finds real social relationships to reduce the suspicion score, and is tamper resistant to synthetic social relationships. It is the white list-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on a variable-size set of attributes. Together, CD and SD can detect more types of attacks, better account for changing legal behaviour, and remove the redundant attributes the work here is motivated by identity crime detection or more specifically, credit application fraud detection (Phua et al. 2005), also known as white-collar crime. Data stream mining involves detecting real-time patterns to produce accurate suspicion scores which are indicative of anomalies. At the same time, the detection system has to handle continuous and rapid examples also known as records tuples, and instances where the recent examples have no class-labels.