Attentive Feature Fusion for Credit Default Prediction

Credit Default Prediction (CDP) has received increasing attention with the prevalence of financial loaning services. Many research efforts have been dedicated to developing novel soft features (i.e. non-financial features), such that they can complement hard features (i.e. financial features) and assist to learn a better default predicting model. But most works combine those features from various sources by just concating them together, and ignore that inappropriate feature fusion methods would compromise model performances. Therefore, in this paper, we propose an Attentive Feature Fusion (AFF) framework for credit default prediction using deep neural networks (DNNs). According to distinct characteristics of the data features, we divide features into multiple groups, and learn their latent representations with separate DNNs, respectively. Then the attention mechanism is applied to integrate those representations together, which allows the important features to be always emphasized and contribute more to the final decision. Experiments on the Lending Club dataset demonstrate that the proposed method can effectively improve the default predicting performances.