We use a dataset of 12 million residential mortgages to investigate the loan default behavior in several European countries. We model the default occurrence as a function of borrower characteristics, loan-specific variables, and local economic conditions. We compare the performance of a set of machine learning algorithms relative to the logistic regression, finding that they perform significantly better in providing predictions. The most important variables in explaining loan default are the interest rate and the local economic characteristics. The existence of relevant geographical heterogeneity in the variable importance points at the need for regionally tailored risk-assessment policies in Europe.

中文翻译：

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用机器学习预测欧洲的贷款违约

我们使用一个包含 1200 万个住宅抵押贷款的数据集来调查几个欧洲国家的贷款违约行为。我们将违约发生建模为借款人特征、贷款特定变量和当地经济状况的函数。我们比较了一组机器学习算法相对于逻辑回归的性能，发现它们在提供预测方面的表现要好得多。解释贷款违约的最重要变量是利率和当地经济特征。不同重要性中存在相关的地理异质性表明欧洲需要针对区域量身定制的风险评估政策。