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_9: 
  @cite_18: Statistical learning theory (SLT) is introduced to improve the embarrassments of anti-money laundering (AML) intelligence collection. A set of unusual behavior detection algorithm is presented in this paper based on support vector machine (SVM) in order to take the place of traditional predefined-rule suspicious transaction data filtering system. It could efficiently surmount the worst forms of suspicious data analyzing and reporting mechanism among bank branches including enormous data volume, dimensionality disorder with massive variances and feature overload.
  @cite_2: Instance-based learning techniques typically handle continuous and linear input values well, but often do not handle nominal input attributes appropriately. The Value Difference Metric (VDM) was designed to find reasonable distance values between nominal attribute values, but it largely ignores continuous attributes, requiring discretization to map continuous values into nominal values. This paper proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric (WVDM). These new distance functions are designed to handle applications with nominal attributes, continuous attributes, or both. In experiments on 48 applications the new distance metrics achieve higher classification accuracy on average than three previous distance functions on those datasets that have both nominal and continuous attributes.
  @cite_17: Suppose you are given some data set drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified value between 0 and 1. We propose a method to approach this problem by trying to estimate a function f that is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabeled data.
summary: In @cite_18 , the authors propose an extension of the support vector machine (SVM) @cite_9 to detect customers’ abnormal behavior. A combination of an RBF kernel (radial basis function) is presented with improvements @cite_17 as a definition for distinct distances @cite_2 and supervised and unsupervised SVM algorithms. An SVM class @cite_9 is a supervised way of learning used to detect value anomalies in a group of data without classes. The advantage of this approach is that you can deal with heterogeneous datasets.