text: We present a data mining approach for profiling bank clients in order to support the process of detection of anti-money laundering operations. We first present the overall system architecture, and then focus on the relevant component for this paper. We detail the experiments performed on real world data from a financial institution, which allowed us to group clients in clusters and then generate a set of classification rules. We discuss the relevance of the founded client profiles and of the generated classification rules. According to the defined overall agent-based architecture, these rules will be incorporated in the knowledge base of the intelligent agents responsible for the signaling of suspicious transactions.
  @cite_6: In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- that of automatic community generation based on uni-party data, the data in which there is no direct or explicit link information available. Consequently, we have proposed a new methodology for Link Discovery based on Correlation Analysis (LDCA). We have used MLC group model generation as an exemplary application of this problem paradigm, and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology, called CORAL. A prototype of CORAL method has been implemented, and preliminary testing and evaluations based on a real MLC case data are reported. The contributions of this work are: (1) identification of the uni-party data community generation problem paradigm, (2) proposal of a new methodology LDCA to solve for problems in this paradigm, (3) formulation of the MLC group model generation problem as an example of this paradigm, (4) application of the LDCA methodology in developing a specific solution (CORAL) to the MLC group model generation problem, and (5) development, evaluation, and testing of the CORAL prototype in a real MLC case data.
summary: Considering the data transformation process, which was especially critical in our work, in @cite_6 , a discretization process was also applied to data set in order to find a more adequate set of clusters. Resources are mapped to n+2 dimensional Euclidean space, n being the customer’s attributes, one dimension for time and another for transactions. Customers’ transactions are projected according to time, accumulating transactions and its frequency to create a histogram. Clusters are created based on segments of the histogram. Analyses of local and global correlations are then applied to detect suspicious patterns. A good way to analyze individual behavior and or group behavior is to examine their operations to detect suspicious behaviors related to abnormal peaks on the histogram. However, when it is necessary to analyze a big number of clients and transactions over a long period of time, it may become difficult to detect suspicious cases, since there might be few peaks or none at all in the histogram.