信用评分:绩效与公平

Stefania Albanesi, Domonkos F. Vamossy
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引用次数: 0

摘要

在美国,信用评分对分配消费债务至关重要,但有关其性能的证据却很少。我们将广泛使用的信用评分与消费者违约的机器学习模型进行对比,发现对借款人,尤其是低分借款人的分类存在重大误差。我们的模型由于在低质量数据方面表现出色,提高了对年轻人、低收入者和少数民族群体的预测准确性,从而再次提高了这些人群的信用等级。我们的研究结果表明,提高信用评分性能可以使人们更公平地获得信贷。
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Credit Scores: Performance and Equity
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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