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Predictably Unequal? The Effects of Machine Learning on Credit Markets
Innovations in statistical technology in functions including credit-screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.
Ketersediaan
| Call Number | Location | Available |
|---|---|---|
| PSB lt.dasar - Pascasarjana (Koleksi Majalah) | 1 |
| Penerbit | USA: The American Finance Association 2022 |
|---|---|
| Edisi | Volume 77, Issue 1, February 2022, Pages 5-47 |
| Subjek | Statistical methods predictability Credit Markets |
| ISBN/ISSN | 1540-6261 |
| Klasifikasi | NONE |
| Deskripsi Fisik | First published : 28 October 2021 |
| Info Detail Spesifik | The Journal of Finance |
| Other Version/Related | Tidak tersedia versi lain |
| Lampiran Berkas |