RISK IDENTIFICATION METHOD AND SYSTEM BASED ON TRANSFER DEEP LEARNING

摘要

The invention relates to a risk identification method and a system on the basis of transfer deep learning. The method comprises: generating vectors for all possible features through prescribed preprocessing, enabling the vector set to act as visible layer input of a first RBM (i.e., Restricted Boltzmann Machine) so as to build an RBM layer; performing transfer learning by using known fraud samples, and carrying out transfer weighted BP tuning on the RBM layer built in the RBM building step; and determining whether the RBM after BP tuning meets prescribed conditions or not, if the RBM meets the prescribed conditions, not requiring to increase the RBM layer and continuing the following step, and if the RBM after BP tuning does not meet the prescribed conditions, repeating the steps of RBM building and transfer weighted BP tuning. A determination model can be built more accurately and emerging fraud means can be better dealt with according to the invention.