Personal Credit Assessment Method Fused with Depth Neural Network
摘要
To improve the accuracy of credit risk assessment,based on the user behavior data of the Internet industry,this paper proposes a personal credit scoring method based on fused deep neural network combining Long Short-Term Memory(LSTM) neural network and Convolutional Neural Network(CNN).The behavior data of each user is encoded to form a matrix that includes the time dimension and the behavior dimension.By fusing the two sub-models,LSTM model and CNN model based on the attention mechanism,the sequence features and local features are extracted from the original user behavior data.Experimental results on real datasets show that the proposed method outperforms the traditional machine learning methods and the single LSTM convolutional neural network method in terms of KS index and AUC index,demonstrating the effectiveness and feasibility of this method in the field of personal credit scoring.