Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups due to its superior performance with low quality data, resulting in a gain in standing for these populations. Our findings suggest that improving credit scoring performance could lead to more equitable access to credit.
@article{arxiv.2409.00296,
title = {Credit Scores: Performance and Equity},
author = {Stefania Albanesi and Domonkos F. Vamossy},
journal= {arXiv preprint arXiv:2409.00296},
year = {2024}
}