<![CDATA[This study investigates the structural dynamics of algorithmic bias within artificial intelligence based credit scoring frameworks and its cascading implications for global financial inclusion. Utilizing a quantitative explanatory design across a cross regional dataset, this empirical inquiry examines how machine learning underwriting architectures process structured financial records alongside non traditional alternative behavioral metrics. The econometric results indicate a severe positive relationship between mathematical model complexity and demographic exclusion, where deep neural networks generate substantial statistical parity differences against protected borrower cohorts by utilizing digital proxy variables. While alternative data features like mobile wallet velocity and utility bill consistency significantly broaden capital accessibility for historically unbanked thin file populations, uncalibrated feature engineering choices systematically replicate pre existing socio economic disparities under the guise of predictive neutrality. To address this conceptual and empirical tension, this paper evaluates post mitigation optimization protocols within contemporary regulatory technology systems. The empirical findings demonstrate that deploying adversarial debiasing frameworks effectively eliminates disparate impact and minimizes equalized odds gaps while fully preserving structural portfolio stability and systemic fraud detection reliability. Â ]]>
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