Credit Scores: Performance and Equity
This addresses inequitable access to credit for underserved populations, though it is incremental as it applies an existing method to a new domain.
The study benchmarked a widely used credit score against a machine learning model for predicting consumer default, finding significant misclassification, especially for low-score borrowers, and improved predictive accuracy for young, low-income, and minority groups.
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.