The scope for the accurate calculation of the Loss Given Default (LGD)
parameter is comprehensive in terms of financial data. In this research, we aim
to explore methods for improving the approximation of realized LGD in
conditions of limited access to the cash-flow data. We enhance the performance
of the method which relies on the differences between exposure values (delta
outstanding approach) by employing machine learning (ML) techniques. The
research utilizes the data from the mortgage portfolio of one of the European
countries and assumes a close resemblance to similar economic contexts. It
incorporates non-financial variables and macroeconomic data related to the
housing market, improving the accuracy of loss severity approximation. The
proposed methodology attempts to mitigate the country-specific (related to the
local legal) or portfolio-specific factors in aim to show the general advantage
of applying ML techniques, rather than case-specific relation. We developed an
XGBoost model that does not rely on cash-flow data yet enhances the accuracy of
realized LGD estimation compared to results obtained with the delta outstanding
approach. A novel aspect of our work is the detailed exploration of the delta
outstanding approach and the methodology for addressing conditions of limited
access to cash-flow data through machine learning models.

基于有限获取现金流数据的条件下，在 Loss Given Default（LGD）参数准确计算的范围内，本研究旨在探索提高实现 LGD 近似值的方法。通过运用机器学习技术，我们改进了依赖于资产暴露差异（delta outstanding approach）的方法，利用欧洲某国的抵押贷款组合数据并结合与房地产市场相关的非金融变量和宏观经济数据，提高了损失严重程度近似值的准确性。我们开发了一个 XGBoost 模型，不依赖现金流数据但比 delta outstanding 方法获得更准确的实现 LGD 估计结果。我们的研究还对 delta outstanding 方法进行了深入探究，并提出了通过机器学习模型解决有限获取现金流数据条件的方法。

改进实现的 LGD 近似：一种处理缺失现金流数据的新框架与 XGBoost

Improving Realized LGD Approximation: A Novel Framework with XGBoost for  Handling Missing Cash-Flow Data

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.

本篇研究使用 Deep Evidence Regression 技术，通过扩展这种方法的学习目标变量来预测违约的损失率。其应用旨在减少信用风险领域的不确定性，通过真实和模拟数据的实验也证明了其应用的有效性。