Suspicious transaction detection model based on Radial Basis Function Neural Network
Jiulong Zhang · Computer Engineering and Applications Journal · 2010
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 pa-rameters of RBF in hidden layer,and RLS algorithm is adopted to update weights of connections between hidden layer and out-put 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 the-oretical and practical value.