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.

翻译：尽管近年来深度学习技术取得了巨大进步，但工业应用中的最新风险预测模型仍严重依赖高度手工调整的分阶段统计学习工具，如梯度提升和随机森林方法。与图像或语音数据不同，真实金融数据具有高维度、稀疏性、嘈杂性和极度不平衡性，这使得深度神经网络模型在训练中极具挑战性，且在实际应用中脆弱易失效。为此，我们提出DeRisk——一种用于真实金融数据信贷风险预测的高效深度学习风险预测框架。DeRisk是首个在性能上超越我们公司生产系统中部署的统计学习方法的风险预测模型。我们还对所提方法进行了广泛的消融研究，揭示了DeRisk经验性成功的最关键因素。