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.
A combination of clustering and MLP (multilayer perceptron) was proposed by @cite_8 . A simple center-based clustering technique is used to detect suspicious cases of money laundering. This technique is based on two main characteristics, which are then used as an MLP creation process entry. The preliminary results show that this approach is efficient. However, the number of characteristics and training patterns is too small and that could affect precision.
@cite_8
2465037653 2128033389 1992348535 1500890010
Most existing model-based approaches to anomaly detection in streaming data are based on decision trees due to their fast construction speed [1]. This paper proposes two fast anomaly detectors based on ensembles of neural networks for evolving data streams. One model is a supervised online learning algorithm involving an ensemble of threaded multilayer perceptrons MLP. The other model is a one-class learning algorithm with an ensemble of threaded autoencoders. The latter model only requires data from the positive class for training and is accurate even when anomalous training data are rare. The models feature an ensemble of multilayer perceptrons or autoencoders from multi-threads which evolve with data streams. Using multi-threads makes the methods highly efficient because both methods process data streams in parallel. Our analysis shows that both methods in streaming data have constant small time complexity and constant memory requirement. When compared with Very Fast Decision Trees VFDT, a state-of-the-art algorithm, our methods performed favorably in terms of detection accuracy and training time for the datasets under consideration. A general method for building and training multilayer perceptrons composed of linear threshold units is proposed. A simple recursive rule is used to build the structure of the network by adding units as they are needed, while a modified perceptron algorithm is used to learn the connection strengths. Convergence to zero errors is guaranteed for any boolean classification on patterns of binary variables. Simulations suggest that this method is efficient in terms of the numbers of units constructed, and the networks it builds can generalize over patterns not in the training set. Multilayer perceptrons (MLPs) with weight values restricted to powers of two or sums of powers of two are introduced. In a digital implementation, these neural networks do not need multipliers but only shift registers when computing in forward mode, thus saving chip area and computation time. A learning procedure, based on backpropagation, is presented for such neural networks. This learning procedure requires full real arithmetic and therefore must be performed offline. Some test cases are presented, concerning MLPs with hidden layers of different sizes, on pattern recognition problems. Such tests demonstrate the validity and the generalization capability of the method and give some insight into the behavior of the learning algorithm. > A commonly encountered problem in MLP (multi-layer perceptron) classification problems is related to the prior probabilities of the individual classes - if the number of training examples that correspond to each class varies significantly between the classes, then it may be harder for the network to learn the rarer classes in some cases. Such practical experience does not match theoretical results which show that MLPs approximate Bayesian a posteriori probabilities (independent of the prior class probabilities). Our investigation of the problem shows that the difference between the theoretical and practical results lies with the assumptions made in the theory (accurate estimation of Bayesian a posteriori probabilities requires the network to be large enough, training to converge to a global minimum, infinite training data, and the a priori class probabilities of the test set to be correctly represented in the training set). Specifically, the problem can often be traced to the fact that efficient MLP training mechanisms lead to sub-optimal solutions for most practical problems. In this chapter, we demonstrate the problem, discuss possible methods for alleviating it, and introduce new heuristics which are shown to perform well on a sample ECG classification problem. The heuristics may also be used as a simple means of adjusting for unequal misclassification costs.