Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact

• URL: http://arxiv.org/abs/2505.13469v1
• Date: Thu, 08 May 2025 19:18:33 GMT
• Title: Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact
• Authors: Aayam Bansal, Harsh Vardhan Narsaria,
• Abstract summary: We quantify how different fairness interventions impact profit margins and default rates.<n>We identify the specific economic conditions under which fair lending becomes profitable.<n>These findings offer practical guidance for designing lending algorithms that balance ethical considerations with business objectives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As financial institutions increasingly rely on machine learning models to automate lending decisions, concerns about algorithmic fairness have risen. This paper explores the tradeoff between enforcing fairness constraints (such as demographic parity or equal opportunity) and maximizing lender profitability. Through simulations on synthetic data that reflects real-world lending patterns, we quantify how different fairness interventions impact profit margins and default rates. Our results demonstrate that equal opportunity constraints typically impose lower profit costs than demographic parity, but surprisingly, removing protected attributes from the model (fairness through unawareness) outperforms explicit fairness interventions in both fairness and profitability metrics. We further identify the specific economic conditions under which fair lending becomes profitable and analyze the feature-specific drivers of unfairness. These findings offer practical guidance for designing lending algorithms that balance ethical considerations with business objectives.

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