CNN-based Feature Cross and Classifier for Loan Default Prediction

Loan default prediction has been playing a key role in credit risk management throughout the years. Existing solutions usually involve classical machine learning classifiers, e.g. logistics and SVM, but most of them need extensive feature engineering such as feature cross which requires plenty of hand-crafted feature design. In this paper, we propose a novel method to implement feature cross based on the convolutional neural network. This method is designed to extract automatically important cross features and generate cross-feature embedding from structured data which reduces the need to generate hand-crafted cross features. The experimental results show that our method can improve the performance of predicting loan default probability compared with the methods based only on classical machine learning algorithms that are widely used in loan default prediction.