How Costly is Noise? Data and Disparities in Consumer Credit






We show that lenders face more uncertainty when assessing default risk of historically under-served groups in US credit markets and that this information disparity is a quantitatively important driver of inefficient and unequal credit market outcomes. We first document that widely used credit scores are statistically noisier indicators of default risk for historically under-served groups. This noise emerges primarily through the explanatory power of the underlying credit report data (e.g., thin credit files), not through issues with model fit (e.g., the inability to include protected class in the scoring model). Estimating a structural model of lending with heterogeneity in information, we quantify the gains from addressing these information disparities for the US mortgage market. We find that equalizing the precision of credit scores can reduce disparities in approval rates and in credit misallocation for disadvantaged groups by approximately half.

Author(s): Laura Blattner, Scott Nelson

Publication Date: 17 May 2021

Publication Site: arXiv

Bias isn’t the only problem with credit scores—and no, AI can’t help



But in the biggest ever study of real-world mortgage data, economists Laura Blattner at Stanford University and Scott Nelson at the University of Chicago show that differences in mortgage approval between minority and majority groups is not just down to bias, but to the fact that minority and low-income groups have less data in their credit histories.

This means that when this data is used to calculate a credit score and this credit score used to make a prediction on loan default, then that prediction will be less precise. It is this lack of precision that leads to inequality, not just bias.


But Blattner and Nelson show that adjusting for bias had no effect. They found that a minority applicant’s score of 620 was indeed a poor proxy for her creditworthiness but that this was because the error could go both ways: a 620 might be 625, or it might be 615.

Author(s): Will Douglas Heaven

Publication Date: 17 June 2021

Publication Site: MIT Tech Review