关键词: 反洗钱, 神经网络, 径向基函数, APC-III聚类算法, RLS算法

Abstract: Aiming at the low detection rate of suspicious transaction at home and abroad in financial field，and with the analysis of Radial Basis Function（RBF） Neural Network，a RBF Neural Network model based on APC-III clustering algorithm and Recursive Least Square（RLS） algorithm for anti-money laundering is proposed.APC-III clustering algorithm is used for determining the parameters of RBF in hidden layer，and 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 the model is proved to have both theoretical and practical value.

Key words: anti-money laundering, Neural Network, Radial Basis Function（RBF）, APC-III clustering algorithm, Recursive Least Square（RLS） algorithm