We assess the role of human discretion in lending outcomes using a randomized, controlled experiment. The lenders in our sample utilize a third party, machine-generated credit model as an input in their decision. We design a new feature for the credit-scoring platform – the slider feature – which invites lenders to incorporate additional discretion in their decision by adjusting the machine-based recommendation. We compare the loan outcomes for treatment lenders that randomly get the slider, relative to a control group. The treatment group's adjustments are predictive of forward looking portfolio characteristics – they show larger declines in future portfolio-level credit risk and larger increases in future sales orders, relative to the control group. The effects of our intervention are more pronounced when borrowers do not have social media accounts and in competitive markets. Our study provides insights about the role of human decisions, given the rapid evolution of machine-based lending models.

我们使用随机对照实验评估人为因素在贷款结果中的作用。我们样本中的贷方将第三方机器生成的信贷模型用作其决策的输入。我们为信用评分平台设计了一项新功能-滑块功能-通过调整基于机器的推荐，邀请贷方在决策中纳入更多的酌处权。我们比较了随机获得滑块的处理贷方相对于对照组的贷款结果。治疗组的调整是对前瞻性投资组合特征的预测-与对照组相比，它们显示了未来投资组合级信用风险的较大下降和未来销售订单的较大增长。当借款人时，我们干预的效果更加明显没有社交媒体帐户，并且在竞争激烈的市场中。鉴于基于机器的贷款模型的快速发展，我们的研究提供了有关人为决策作用的见解。