AI-Based Online P2P Lending Risk Assessment On Social Network Data With Missing Value

This study explores an important issue of data mining - missing value - by using Neural Network for missing value imputation. We focus on social network data for P2P lending risk assessment. However, social network data usually contain lots of missing values so that predicting accurate credit level is not feasible. We propose a neural network based approach and adapt Generative Adversarial Network concept to generate the missing values. The Generator could output the possible values of the missing values; while the Discriminator could judge if the output values from Generator is appropriate or not for further training. We use real-world P2P data without missing values and the construct datasets with random missing values for evaluation.