Communal Detection of Implicit Personal Identity Streams

The purpose of this paper is to outline some of the major developments of an identity crime/fraud stream mining system. Communal detection is about finding real communities of interest. The algorithm itself is unsupervised, single-pass, differentiates between normal and anomalous links, and mitigates the suspicion of normal links with a dynamic global whitelist. It is part of the important and novel communal detection framework introduced here for monitoring implicit personal identity streams. For each incoming identity example, it creates one of three types of single link (black, white, or anomalous) against any previous example within a set window. Subsequently, it integrates possible multiple links to produce a smoothed numeric suspicion score. In a principled stream-like fashion and using eighteen different parameter settings replicated over three large window sizes, this paper highlights and discusses significant score results from mining a few million recent credit applications