Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete-time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate age–period–cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage), and environment (e.g., economic, operational, and social effects). These can be built as general models or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage dataset. This novel framework can be adapted by practitioners in the financial industry to improve modeling, estimation, and assessment of credit risk.

中文翻译：

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用于信用风险年龄-周期-队列分析的神经网络离散时间生存模型

生存模型在信用风险估计中已变得流行。当前大多数信用风险生存模型都使用基础线性模型。这在可解释性方面是有益的，但对于现实生活中的应用来说是有限制的，因为它无法发现数据中隐藏的非线性和相互作用。本研究使用带有嵌入式神经网络的离散时间生存模型作为违约时间的估计器。这提供了表达变量之间的非线性和相互作用的灵活性，从而允许模型具有更好的整体模型拟合。此外，神经网络用于估计年龄-周期-队列（APC）模型，以便违约风险可以分解为贷款期限（到期）、起源（年份）和环境（例如，经济、运营和环境）的时间组成部分。社会影响）。这些模型可以构建为通用模型，也可以构建为针对特定客户群的本地 APC 模型。本地 APC 模型揭示了不同客户群体的特殊情况。由于预计环境风险与宏观经济状况有很强的关系，因此相应的 APC 识别问题通过正则化和将分解的环境时间风险分量拟合到宏观经济数据相结合来解决。在大型公开的美国抵押贷款数据集上进行测试时，我们的方法被证明是有效的。金融行业从业者可以采用这种新颖的框架来改进信用风险的建模、估计和评估。