UQ for Credit Risk Management: A deep evidence regression approach

• URL: http://arxiv.org/abs/2305.04967v2
• Date: Wed, 17 May 2023 14:25:03 GMT
• Title: UQ for Credit Risk Management: A deep evidence regression approach
• Authors: Ashish Dhiman
• Abstract summary: 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. We demonstrate the application of our approach to both simulated and real-world data.
• Score: 3.42658286826597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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