Abstract
Using a randomized experiment in auto lending, we show that algorithmic underwriting outperforms the human underwriting process, resulting in 10.2% higher loan profits and 6.8% lower default rates. The machine performance is more stable across various risk dimensions and loan characteristics, whereas the performance of human underwritten loans largely declines for riskier and more complex loans. Moreover, the performance difference is more pronounced at underwriting thresholds with a high potential for agency conflict. These results are consistent with algorithmic underwriting mitigating agency conflicts and humans' limited capacity for analyzing complex problems.
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