Abstract: Personal credit is the most important factor for banks to measure individual compliance risk.In recent years,with the increasing demand for borrowing in China,the traditional way of making credit evaluation,which is merely based on credit card transaction information,cannot fully meet the development needs of the banking industry.Therefore,this paper proposed to use the big data of personal consumption in bank as the important feature information to construct a richer user image.In order to overcome the dimensional curse and noise caused by the financial big data,a modified deep learning evaluation algorithm based on stacked denoising autoencoder neural network is proposed by considering the correlation of feature data and the truncated Karhunen-Loève expansion is applied as the noise input term,then a series of related data experiments are conducted on big data platform of a commercial bank.The experimental results show that,compared with the risk evaluation just based on credit card transaction information,the K-S value that measure the positive and negative sample resolution based on big data of bank improves 11%;the improved stack denoising autoencoder neural network method has better risk assessment results and the accuracy rate is increased by about 3% compared with the original model,thus validating the effectiveness of credit risk assessment in the big data environment of bank.

Key words: Big data, Credit risk assessment, Deep learning, Dimensional curse, Feature selection, Stacked denoising