This study uses data on consumer credit provided by a German retail and trading company along with generalized additive models to analyze nonlinear relationships and their effect on predicting the probability of default in the context of consumer credit scoring. In particular, this study examines which aspects of the contract and which characteristics of the debtor are nonlinearly related to the probability that specific debtors will default on the loans they are seeking to obtain. The findings of our analysis provide clear empirical evidence that certain contract and debtor characteristics have nonlinear and nonmonotonic effects on the probability that a specific debtor will default on their consumer credit. The results of our analysis and the forms that these nonlinear relationships take are interpreted with regard to their economic relevance. Specifically, the study shows that the nonlinear relationships identified are economically relevant because they take into account the costs of a range of misclassification errors. On the basis of this evidence, the study recommends that analysts include nonlinear relationships in the models they design to predict consumer default, although including such relationships increases the complexity of the models that are applied for this purpose.

中文翻译：

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高违约分期投资组合违约逻辑模型中的非线性关系

本研究使用德国零售和贸易公司提供的消费者信用数据以及广义可加模型来分析非线性关系及其在消费者信用评分背景下对预测违约概率的影响。特别是，本研究考察了合同的哪些方面和债务人的哪些特征与特定债务人拖欠他们寻求获得的贷款的可能性呈非线性相关。我们的分析结果提供了明确的经验证据，表明某些合同和债务人特征对特定债务人违约消费信贷的概率具有非线性和非单调影响。我们的分析结果和这些非线性关系的形式是根据它们的经济相关性来解释的。具体而言，该研究表明，确定的非线性关系在经济上是相关的，因为它们考虑了一系列错误分类错误的成本。根据这一证据，该研究建议分析师在他们设计的模型中加入非线性关系来预测消费者违约，尽管包括这种关系会增加用于此目的的模型的复杂性。