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
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.