Machine Learning has invariantly found its way into various Credit Risk applications. Due to the intrinsic nature of Credit Risk, quantifying the uncertainty of the predicted risk metrics is essential, and applying uncertainty-aware deep learning models to credit risk settings can be very helpful. In this work, we have explored the application of a scalable UQ-aware deep learning technique, Deep Evidence Regression and applied it to predicting Loss Given Default. We contribute to the literature by extending the Deep Evidence Regression methodology to learning target variables generated by a Weibull process and provide the relevant learning framework. We demonstrate the application of our approach to both simulated and real-world data.

翻译：机器学习已不可避免地渗透到各种信用风险应用中。由于信用风险的内在特性，量化预测风险指标的不确定性至关重要，将不确定性感知的深度学习模型应用于信用风险场景将大有裨益。本研究探索了一种可扩展的不确定性感知深度学习技术——深度证据回归在预测违约损失率中的应用。我们通过将深度证据回归方法扩展到学习由威布尔过程生成的目标变量，并提供了相关的学习框架，从而对现有文献作出贡献。我们展示了该方法在模拟数据与真实数据上的应用效果。