Algorithmic Fairness and Financial Inclusion in AI-Driven Insurance Underwriting: A Systematic Review
Keywords:
Financial Inclusion, Insurance Underwriting, Algorithmic Fairness, Insurance Governance, Responsible Innovation, Reduced Inequality, FAIR FrameworkAbstract
Artificial intelligence (AI) is reshaping insurance underwriting, yet its implications for financial inclusion remain contested. This study aims to examine how AI-driven underwriting influences financial inclusion, analyse the ethical challenges associated with its use, evaluate the adequacy of existing governance responses, and identify priorities for future research. Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses, the study synthesised 48 eligible publications published between 2020 and 2026, supported by bibliometric mapping to trace thematic structure and research patterns. The findings reveal AI to be associated with improved underwriting efficiency, predictive capacity, as well as operational reach; yet, such gains are often accompanied by bias, opacity and exclusionary outcomes, particularly for underserved populations. Across the reviewed corpus, financial inclusion does not emerge as an automatic consequence of technical innovation; instead, it depends on: quality of data, explainability of AI models, inclusiveness of system design, the strength of regulatory oversight. Governance mechanisms were found to be fragmented and uneven across jurisdictions, albeit the evidence base remains geographically narrow, as well as methodologically weighted toward technical performance rather than lived inclusion outcomes. The study concludes that AI can support fairer insurance access only when inclusion is treated as a foundational design and governance principle rather than a secondary compliance concern. Policy-wise, the study recommended a proposed "FAIR" framework for inclusive AI underwriting, centred on Fair Data, Accountable Models, Inclusive Design, and Regulatory Oversight.
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