This paper examines the impact of large-scale alternative data on predicting consumer delinquency. Using a proprietary double-blinded test from a traditional lender, we find that the big data credit score predicts an individual’s likelihood of defaulting on a loan with 18.4% greater accuracy than the lender’s internal score. Moreover, the impact of the big data credit score is more significant when evaluating borrowers without public credit records. We also provide evidence that big data have the potential to correct financial misreporting.

中文翻译：

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在消费者信用评估中解读大数据

本文研究了大规模替代数据对预测消费者违法行为的影响。通过使用传统贷方的专有双盲测试，我们发现大数据信用评分可以预测个人违约的可能性，其准确性比贷方的内部评分高18.4％。此外，在评估没有公共信用记录的借款人时，大数据信用评分的影响更为显着。我们还提供证据表明大数据有可能纠正财务错误报告。