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@article{Fuster:2022:10.1111/jofi.13090,
author = {Fuster, A and Goldsmith-Pinkham, P and Ramadorai, T and Walther, A},
doi = {10.1111/jofi.13090},
journal = {The Journal of Finance},
pages = {5--47},
title = {Predictably unequal? The effect of machine learning on credit markets},
url = {http://dx.doi.org/10.1111/jofi.13090},
volume = {77},
year = {2022}
}

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TY  - JOUR
AB  - Innovations  in  statistical  technology  have  sparked  concerns  about  distributional  impacts across categories such as race and gender.  Theoretically, as statistical technology improves, distributional consequences depend on how changes in functional forms interact with cross-category distributions of observable characteristics.  Using detailed administrative data on US mortgages, we embed the predictions of traditional logit and more sophisticated machine-learning default prediction models into a simple equilibrium credit model.  Machine learning models  slightly  increase  credit  provision  overall,  but  increase  rate  disparity  between  and within groups; effects mainly arise from flexibility to uncover structural relationships between default  and  observables,  rather  than  from  triangulation  of  excluded  characteristics.   We predict  that  Black  and  Hispanic  borrowers  are  disproportionately  less  likely  to  gain  from new technology.
AU  - Fuster,A
AU  - Goldsmith-Pinkham,P
AU  - Ramadorai,T
AU  - Walther,A
DO  - 10.1111/jofi.13090
EP  - 47
PY  - 2022///
SN  - 0022-1082
SP  - 5
TI  - Predictably unequal? The effect of machine learning on credit markets
T2  - The Journal of Finance
UR  - http://dx.doi.org/10.1111/jofi.13090
UR  - http://hdl.handle.net/10044/1/85765
VL  - 77
ER  -

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