Corporate-Governance-Driven Algorithmic Fairness in SME Fintech Lending: A Systematic Literature Review with Expert Validation - Chloe Victoria; Daniel Müller (University of Science and Computer Technology, 2026)
The rapid growth of fintech lending has reshaped financial access for SMEs through AI-driven credit assessment platforms. While promising greater efficiency, these systems create significant algorithmic bias risks, which poor corporate governance and lack of transparency in model development usually exacerbate. Based on this, the study develops and validates an integrated conceptual framework that incorporates corporate governance principles with mechanisms for algorithmic fairness to foster ethical outcomes in SME fintech lending. We follow a two-phase approach, wherein, first, an SLR of 45 peer-reviewed publications for the period from 2022 to 2025 was conducted, followed by structured validation with five domain experts in AI ethics, corporate governance, and fintech regulation. Our analysis revealed four foundational governance pillars, viz., Accountability, Transparency, Fairness, and Compliance. Expert validation established strong relevance and practical utility for the framework, with a mean score of 4.6/5. This study hence proposes a novel, validated model to equip fintech managers and regulators with a governance-based approach to tackling algorithmic bias and, in turn, positions them better to engender trust and financial inclusion.