Credit Scores: Performance and Equity

• URL: http://arxiv.org/abs/2409.00296v1
• Date: Fri, 30 Aug 2024 23:36:02 GMT
• Title: Credit Scores: Performance and Equity
• Authors: Stefania Albanesi, Domonkos F. Vamossy,
• Abstract summary: We benchmark a widely used credit score against a machine learning model of consumer default. We find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for young, low-income, and minority groups.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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