Efficient forecasting and uncertainty quantification for large-scale account level Monte Carlo models of debt recovery
针对非担保消费贷款组合的债务回收预测，提出高效分配计算资源和量化不确定性的方法，通过账户级方差的无偏估计构建总体方差稳健估计量，并在实际类似模型上验证。
Abstract The state-of-the-art in forecasting debt recovery from portfolios of non-performing unsecured consumer loans is to use stochastic models of payment behaviour of individual customers. Monte Carlo simulation of these models can enable forecasting of collections, where computational complexity arises from the very large number of heterogeneous accounts. We aim to solve 2 problems: efficient allocation of computational resources and quantification of uncertainty. We show that robust estimators of population-level variance can be constructed using unbiased estimators of the variance of individual accounts. The proposed methods are demonstrated through application to a model similar to those used in practice.