Financial Inclusion or Digital Discrimination? Algorithmic Credit Decisions and Gender Inequalities: A Scoping Review

The rapid expansion of financial technologies has repositioned algorithmic credit decision systems as central gatekeepers of economic opportunity, raising a fundamental sociological question: Do these systems broaden financial inclusion or do they reproduce—and potentially amplify—gender inequalities under a veneer of technical neutrality? This scoping review maps the scientific literature at the intersection of algorithmic credit decisions, financial inclusion, and gender, following the PRISMA-ScR protocol and combining bibliometric and thematic analysis. A corpus of 279 peer-reviewed documents (English-language articles and reviews, 2022–2026) indexed in Scopus was charted, exhibiting a compound annual growth rate of 31.6%, 4445 accumulated citations (h-index = 32), and a highly skewed citation distribution (Gini = 0.819). Production is led by the Global South—India (n = 64), Indonesia (n = 32), and South Africa (n = 19)—while co-word and semantic cluster analysis reveals five research fronts organized around digital financial literacy, gender and banking, artificial intelligence and blockchain, microfinance and the Sustainable Development Goals, and algorithmic risk assessment. The gender-focused subcorpus (n = 58; 20.8%) and the algorithmic credit subcorpus (n = 49; 17.6%) intersect in only 11 documents (3.9%), exposing a critical research gap precisely where the promise of inclusion and the risk of discrimination collide. This overlap is no larger than independence would predict (expected = 10.2; Fisher’s exact p = 0.85) and stays below 5% under alternative tagging dictionaries, while documents authored exclusively by Global South institutions receive 44% fewer citations than comparable documents. Evidence charted from the corpus documents both inclusionary effects (expanded access for thin-file borrowers, reduced transaction costs) and exclusionary dynamics (gender-miscalibrated credit scores, predatory digital lending, algorithmic opacity, and intersectional data gaps). Rurality, informal employment, ethnicity, and migration status are rarely analyzed together with gender, and an integrative conceptual model links structural conditions, intersecting social positions, data regimes, and algorithmic pipelines to inclusionary, predatory, and exclusionary outcomes. The review proposes an agenda oriented toward algorithmic auditing with gender-disaggregated data, regulatory frameworks for fairness in credit scoring, and the integration of feminist data perspectives into fintech design, arguing that financial inclusion and digital discrimination are not mutually exclusive outcomes but coexisting logics of the same socio-technical infrastructure.