Rise of the Machines: The Impact of Automated Underwriting
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Cited by:
- Christophe Hurlin & Christophe Perignon & Sébastien Saurin, 2021.
"The Fairness of Credit Scoring Models,"
Working Papers
hal-03501452, HAL.
- Christophe Hurlin & Christophe P'erignon & S'ebastien Saurin, 2022. "The Fairness of Credit Scoring Models," Papers 2205.10200, arXiv.org, revised Feb 2024.
- Christophe HURLIN & Christophe PERIGNON & Sébastien SAURIN, 2021. "The Fairness of Credit Scoring Models," LEO Working Papers / DR LEO 2912, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
- Tobias Berg & Andreas Fuster & Manju Puri, 2022.
"FinTech Lending,"
Annual Review of Financial Economics, Annual Reviews, vol. 14(1), pages 187-207, November.
- Berg, Tobias & Puri, Manju, 2021. "FinTech Lending," CEPR Discussion Papers 16668, C.E.P.R. Discussion Papers.
- Tobias Berg & Andreas Fuster & Manju Puri, 2021. "FinTech Lending," Swiss Finance Institute Research Paper Series 21-72, Swiss Finance Institute.
- Tobias Berg & Andreas Fuster & Manju Puri, 2021. "FinTech Lending," NBER Working Papers 29421, National Bureau of Economic Research, Inc.
More about this item
JEL classification:
- D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BAN-2021-02-15 (Banking)
- NEP-BIG-2021-02-15 (Big Data)
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