Debiasing Alternative Data for Credit Underwriting Using Causal Inference

• URL: http://arxiv.org/abs/2410.22382v2
• Date: Thu, 31 Oct 2024 17:12:27 GMT
• Title: Debiasing Alternative Data for Credit Underwriting Using Causal Inference
• Authors: Chris Lam,
• Abstract summary: Alternative data provides valuable insights for lenders to evaluate a borrower's creditworthiness. But some forms of alternative data have historically been excluded from credit underwriting because it could act as an illegal proxy for a protected class. We propose a method for applying causal inference to a supervised machine learning model to debias alternative data so that it might be used for credit underwriting.
• Score: 0.0
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• Abstract: Alternative data provides valuable insights for lenders to evaluate a borrower's creditworthiness, which could help expand credit access to underserved groups and lower costs for borrowers. But some forms of alternative data have historically been excluded from credit underwriting because it could act as an illegal proxy for a protected class like race or gender, causing redlining. We propose a method for applying causal inference to a supervised machine learning model to debias alternative data so that it might be used for credit underwriting. We demonstrate how our algorithm can be used against a public credit dataset to improve model accuracy across different racial groups, while providing theoretically robust nondiscrimination guarantees.

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