This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.

本研究评估了广泛的机器学习技术，例如深度学习、提升和支持向量回归，以预测一家德国第三方公司从电信部门购买的 65,000 多个违约消费信贷的回收率。加权绩效衡量标准是基于默认暴露值定义的，用于比较收集率模型。本文中提出的方法对于第三方公司管理其购买的违约信贷组合的风险很有用。主要发现是，我们调查的其中一种机器学习模型，即深度学习模型，在样本外的表现明显优于违约信用的收购方基于加权绩效衡量标准可以使用的所有其他方法。通过使用未加权的性能指标，深度学习和提升性能相似。此外，我们发现在使用深度学习时，使用具有较大数据集比例的训练集并不会显着提高预测精度。一般结论是，深度学习是一种潜在的信用风险管理性能增强工具。