Technology is Changing Lending: Implications for Research (A Discussion of Costello, Down, and Mehta (2020))

Andrew Sutherland
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Abstract

Costello, Down, and Mehta (2020) trace their slider intervention to deviations from the credit line amount recommended by a credit scoring model. The deviations are followed by larger delinquency declines and bigger sales orders, and Costello et al. interpret these results using discretion-based theories. However, incremental deviations are concentrated on newer clients rather than those the lender has accumulated soft information about. Deviations also appear larger for public than private borrowers. My discussion evaluates whether these results align with discretion-based theories, and explores alternative interpretations based on salience and unique aspects of the trade credit setting. Differences in interpretation aside, the evidence is informative about technological advances in commercial lending. I conclude with an overview of several recent advances and discuss the implications for lending research.
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技术正在改变借贷:对研究的影响(讨论Costello, Down和Mehta (2020))
Costello, Down和Mehta(2020)将他们的滑块干预追溯到偏离信用评分模型推荐的信用额度。这些偏差之后是更大的犯罪下降和更大的销售订单,Costello等人使用基于自由裁量权的理论解释了这些结果。然而,增量偏差集中在新客户身上,而不是那些贷款人已经积累了软信息的客户。公共借款人的偏差似乎也大于私人借款人。我的讨论评估了这些结果是否与基于自由裁量权的理论相一致,并探讨了基于贸易信贷设置的显著性和独特性的其他解释。撇开解释上的差异不谈,这些证据对商业贷款的技术进步提供了有益的信息。最后,我概述了最近的几项进展,并讨论了对贷款研究的影响。
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