融合时空模型与梯度提升，提升房贷违约预测精度。
A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios
- 用树增强与潜在时空高斯过程建模非线性关系和空间时间相关性。
- 在美国家庭抵押贷款数据上，违约概率与组合损失预测更准确。
- 适合关注信贷风险建模、需处理复杂时空依赖的金融从业者。
我们提出一种新型机器学习信用风险模型，结合梯度提升与潜在时空高斯过程，考虑脆弱性相关性。该方法可灵活建模预测变量间的非线性及交互作用，并捕捉可观测变量无法解释的空间时间变异。我们展示了高效估算与预测的实现方式。在大规模美国抵押贷款信用风险数据集上的应用表明，与传统独立线性风险模型及线性时空模型相比，本方法在个体贷款违约概率预测和贷款组合损失分布预测上均表现更优。通过机器学习可解释性工具分析，性能提升主要源于预测变量中的强交互效应和非线性特征，以及空间时间脆弱性效应的存在。
原文摘要 · Abstract (English)
We introduce a novel machine learning model for credit risk by combining tree-boosting with a latent spatio-temporal Gaussian process model accounting for frailty correlation. This allows for modeling non-linearities and interactions among predictor variables in a flexible data-driven manner and for accounting for spatio-temporal variation that is not explained by observable predictor variables. We also show how estimation and prediction can be done in a computationally efficient manner. In an application to a large U.S. mortgage credit risk data set, we find that both predictive default probabilities for individual loans and predictive loan portfolio loss distributions obtained with our novel approach are more accurate compared to conventional independent linear hazard models and also linear spatio-temporal models. Using interpretability tools for machine learning models, we find that the likely reasons for this outperformance are strong interaction and non-linear effects in the predictor variables and the presence of spatio-temporal frailty effects.
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