信用评分：性能与公平性
Credit Scores: Performance and Equity
AI总结：
本文通过将广泛使用的信用评分与机器学习违约模型进行基准对比，发现现有评分对低评分人群存在显著误分类，而新模型能提升年轻、低收入和少数族裔群体的预测准确性，从而促进更公平的信贷获取。
摘要：
信用评分在美国消费者债务分配中至关重要，但关于其性能的证据却很少。我们将一种广泛使用的信用评分与一个消费者违约的机器学习模型进行基准对比，发现借款人被显著错误分类，尤其是那些低评分人群。由于我们的模型在处理低质量数据时性能更优，因此提高了年轻、低收入和少数族裔群体的预测准确性，从而提升了这些群体的信用地位。我们的研究结果表明，改进信用评分性能可以带来更公平的信贷获取机会。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
