A Two-Stage Dynamic Credit Risk Assessment System

Credit risk assessment has been thought of as a critical factor in financial companies and banks in the history of development economics. Recently, there has been renewed interest in credit risk assessment using deep learning methods. However, previous studies have not fine-grained dealt with static and dynamic features, which limits their effectiveness. Thus, in this paper, we present a two-stage model using FeedForward Neural Network(FNN) and Recurrent Neural Network(RNN). First, we design the aggregation layer to extract representative information from the static feature at time T. Second, the distinct moment representation constructs the dynamic features of a client. The dynamic features could be learned by the RNN layer. Experimental results on the real-world dataset show its superiority over various baselines.