 Machine + man: A field experiment on the role of discretion in augmenting AI-based lending modelsAnna Costello, Andrea K. Down and Mihir N. Mehta Journal of Accounting and Economics, 2020, vol. 70, issue 2 Abstract: 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. Keywords: Relationship lending; Discretion; Machine-learning; fintech; Artificial intelligence (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jaecon:v:70:y:2020:i:2:s0165410120300628 DOI: 10.1016/j.jacceco.2020.101360 Access Statistics for this article Journal of Accounting and Economics is currently edited by J. L. Zimmerman, S. P. Kothari, T. Z. Lys and R. L. Watts More articles in Journal of Accounting and Economics from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.