Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Albanesi, Stefania, Vamossy, Domonkos F.
Institution: Center for Economic and Policy Research
Language: English
Subfield: Accounting
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W4402953318