Evaluating Algorithmic Fairness in Small Business Lending Models Across Urban and Rural Banking Markets
Keywords:
Algorithmic fairness, small business lending, rural banking, urban credit models, machine learning bias, financial inclusionAbstract
This study investigates algorithmic fairness in small business lending models across urban and rural banking environments. As machine learning tools increasingly shape credit decisions, concerns over fairness, bias, and discrimination intensify. Disparities in data availability, socioeconomic indicators, and digital infrastructure between rural and urban settings pose significant challenges. The research highlights fairness metrics, bias mitigation strategies, and proposes equitable model evaluation frameworks. Emphasis is placed on understanding the intersection of geography and credit access, particularly for marginalized small enterprises.
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