Session Chair: Bryan Lim, The University of Melbourne
Kristle Cort├ęs, University of New South Wales
Phong Ngo, Australian National University

Rise of the Machines: The Impact of Automated Underwriting

Mark Jansen, The University of Utah
Hieu Nguyen, The University of Utah
Amin ShamsThe Ohio State University

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.

Banking on Carbon: Corporate Lending and Cap-and-Trade Policy

Ivan Ivanov, Federal Reserve Board
Mathias KruttliFederal Reserve Board
Sumudu Watugala, Cornell University

We estimate the effect of carbon pricing policy on bank credit to firms with greenhouse gas emissions. Our analyses exploit the geographic restrictions inherent in the California cap-and-trade bill and a discontinuity in the embedded free-permit threshold of the federal Waxman-Markey cap-and-trade bill. Affected high-emission firms face shorter loan maturities, lower access to permanent forms of bank financing, higher interest rates, and higher participation of shadow banks in their lending syndicates. These effects are concentrated among private firms, suggesting banks are less concerned about the policies' impact on public firms. Overall, banks quickly mitigate their exposure to climate transition risks.

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