  Despite the tremendous advances achieved over the past years by deep learning
techniques, the latest risk prediction models for industrial applications still
rely on highly handtuned stage-wised statistical learning tools, such as
gradient boosting and random forest methods. Different from images or
languages, real-world financial data are high-dimensional, sparse, noisy and
extremely imbalanced, which makes deep neural network models particularly
challenging to train and fragile in practice. In this work, we propose DeRisk,
an effective deep learning risk prediction framework for credit risk prediction
on real-world financial data. DeRisk is the first deep risk prediction model
that outperforms statistical learning approaches deployed in our company's
production system. We also perform extensive ablation studies on our method to
present the most critical factors for the empirical success of DeRisk.
