Fraud Detection on Large Scale Social Networks

The incredible growth of the internet use for all kinds of businesses has generated at the same time an increase of fraudulent activities, which calls for developing new methods and tools for detecting fraud and other crimes against banks and customers. Fraud detection needs to analyze and link data, which are gathered from heterogeneous data repositories, and to address problem solving algorithms optimization and parallelization, new knowledge representation paradigms, association mechanisms for linking data, and graph analysis for clustering and partitioning. We present in this paper the motivation of our study and the first steps of the work. We will focus on the emergence of new coding models based on MapReduce and SQL extensions, and on graphs paths issues.