A Study on Human-Centric Explainable Ai Auditing Systems for Financial Inclusion, Digital Empowerment, and Trust-Building in Emerging Economies
DOI:
https://doi.org/10.29070/w0j61s95Keywords:
Human-Centric XAI, Financial Auditing, Financial Inclusion, Digital Empowerment, Trustworthy AIAbstract
This paper investigates human-centred XAI resolutions for financial inclusiveness, digital enablers and responsibility and confidence in AI-enabled finance in emerging markets. It examines how AI aids financial audit across credit, fraud, regulation, audit, compliance, digital payment services and risk classification; and it enables exclusionary hazards of unequal data, unaccountable tools, inscrutable docs and opaque management; ultimately aiming to develop an Human-Centred XAI Trust Structure for contextually-appropriate audibility and contestability in identifying if financial audit is technically reliable, intelligible, challengeable, socially inclusive and institutionally accountable. It employs qualitative secondary data-based conceptual research using an integrative literature review, thematic investigation, qualitative content analysis, stakeholder examination, case-based comparative analysis, risk-control activity mapping and conceptual synthesis; drawing evidence from open access academic journals, policy papers, financial innovation policies, AI ethical codes, fintech management policies, emerging markets exemplars. Its essentials signify that AI-enabled financial audit provides dual accountability due to the double nature of financial activity and its automated audit; and technical transparency even cannot generate peoples' understanding, capability, correction and inquiry of the automated decisions. Financial digital exclusion can arise from incomplete and unrepresentative data, proxy-based bias, uneven error rate, opaque docs, natural language translation, human fragility, disintegrated accountability lines and weak grievance response. The context-sensitive human-centred trust structure weights up contextual perception, data and modeller integrity, human-centred explication, auditability, participation, inclusive management and empowerment outcomes. It documents Stakeholder-centred explication, responsible decisions, Trust calibration and Guide to contextual implementation for responsible AI in digital finance.
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References
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