Combining Social Network Analysis with Semi-supervised Clustering: a case study on fraud detection

At time of crisis, when fraud permanently frightens the basis of modern societies, the existence of effective tools to prevent it, or just to identify it in time, is critical. However, the detection of fraud is naturally impaired (among other issues) by the difficulty on labelling data, due to the cost of identifying and attest fraud. Moreover, the inability to incorporate domain knowledge in the mining process makes classifiers to use inadequate attributes to distinguish entities, ignoring most of existing relevant information, like entities’ social relations. In this paper, we propose a new methodology for enriching semi-supervised clustering with information collected through the analysis of social networks. The methodology is then applied on tax fraud detection and assessed by measuring the impact of this enrichment in the accuracy of semi-supervised clustering methods.