Under the International Financial Reporting Standards (IFRS) 9, credit losses ought to be recognised timeously and accurately. This requirement belies a certain degree of dynamicity when estimating the constituent parts of a credit loss event, most notably the probability of default (PD). It is notoriously difficult to produce such PD-estimates at every point of loan life that are adequately dynamic and accurate, especially when considering the ever-changing macroeconomic background. In rendering these lifetime PD-estimates, the choice of modelling technique plays an important role, which is why we first review a few classes of techniques, including the merits and limitations of each. Our main contribution however is the development of an in-depth and data-driven tutorial using a particular class of techniques called discrete-time survival analysis. This tutorial is accompanied by a diverse set of reusable diagnostic measures for evaluating various aspects of a survival model and the underlying data. A comprehensive R-based codebase is further contributed. We believe that our work can help cultivate common modelling practices under IFRS 9, and should be valuable to practitioners, model validators, and regulators alike.

翻译：根据《国际财务报告准则第9号》（IFRS 9），信用损失应当被及时且准确地确认。这一要求在估计信用损失事件的构成要素（尤其是违约概率（PD））时，隐含了对动态性的特定需求。众所周知，在贷款全周期的每个时点生成具备充分动态性和准确性的PD估计极为困难，尤其在不断变化的宏观经济背景下。在生成这些全周期PD估计时，建模技术的选择至关重要，因此我们首先回顾了几类技术方法，包括各自的优势与局限。然而，我们的主要贡献在于开发了一个深入且数据驱动的教程，该教程使用了一类称为离散时间生存分析的技术。本教程附带了一套多样化的可复用诊断指标，用于评估生存模型及其基础数据的各个方面。此外，我们还贡献了一个基于R语言的完整代码库。我们相信，这项工作有助于培育IFRS 9下的通用建模实践，并对从业者、模型验证者及监管机构具有重要价值。