An Application Research On Graph Based Link Discovery In Anti-Money Laundering

Financial crimes always hide among large number of exchanges between normal accounts. Nowadays money laundering crimes trend to be collectivized, criminals not only use several accounts to transfer black money, but also use account groups to conceal their criminal pattern. The existing technologies and approaches in financial supervising can't reveal these exceptional fund flow efficiently. Aiming at this problem, graph based link discovery technology, which compresses the fund exchange graph by frequent subgraphs, can detect the criminal pattern of groups with illegal fund exchanges.According to social network analysis theory, a large number of abnormal behaviors are always hidden in the normal mode of behavior. Corresponding to the field of financial supervising is to remove the normal structure of the transaction, which highlights unusual structure. It's a proceeding of cutting frequent subgraphs from the fund exchange graph. So the approach of graph based link analysis is based on frequent subgraph searching algorithm. In the basis of Apriori idea, this paper presents a frequent subgraph finding algorithm. The algorithm begins from a single vertex, generate a set of candidates in k ranks through iteration.When candidates of frequent subgraphs generated, there are two kinds of illicit transactions discovering approaches: unsupervised mode and method of supervised mode. After source data preprocessing and financial transaction graph building, unsupervised method adopt a measure to calculate the value of frequent subgraphs in candidates. The best candidate is the one which compress the graph to the least size. The method of compressing is replacing the instances of the best subgraph in the graph by a vertex. Finally the unusual structure of fund transactions, called money-laundering flow, formation after repeating this proceeding.Supervised method let the domain knowledge in the field of financial supervising, which is described as positive set and negative set, induct the compress proceeding. Supervised method also formations the money-laundering flow by iteration. Compared with unsupervised mode, supervised method measures the candidates of frequent subgraphs by their compression level to positive set and negative set. And the two set also take part in the iteration of compressing.According to these approaches, a application system prototype is designed to prove the validity of the approaches,and result visualization is also studied in the system.