A RBF neural network model for anti-money laundering

Money laundering (ML) is a serious crime which makes it necessary to develop detection methods in transactions. Some researches have been carried on, but the problem is not thoroughly solved. Aiming at the low detection rate of suspicious transaction at home and abroad in financial field, and with an analysis of radial basis function (RBF) neural network, we propose a radial basis function neural network model based on APC-III clustering algorithm and recursive least square algorithm for anti-money laundering (AML). APC-III clustering algorithm is used for determining the parameters of radial basis function in hidden layer, and recursive least square (RLS) algorithm is adopted to update weights of connections between hidden layer and output layer. The proposed method is compared against support vector machine (SVM) and outlier detection methods, which show that the proposed method has the highest detection rate and the lowest false positive rate. Thus our method is proved to have both theoretical and practical value for anti-money laundering.