A Hybrid Deep Learning Model for Consumer Credit Scoring

Consumer credit scoring is an essential part of credit risk management in the fast-growing consumer finance industry and various data mining techniques have been proposed and used on it. Recently, deep learning techniques have gained significant popularity and shown excellent performance in many fields such as image recognition, computer vision and so on. In this paper, we try to take the advantage of deep learning and introduce it into consumer credit scoring. We propose a hybrid model that combines the well-known convolutional neural network with the feature selection algorithm Relief. Experiments are carried on a real-world dataset from a Chinese consumer finance company, and the results show that the proposed model gets superior performance in comparison with other benchmark models such as logistic regression and random forest.