Neutralizing Structural Inequality in the Nigerian FinTech Sector

• URL: http://arxiv.org/abs/2607.10317v1
• Date: Sat, 11 Jul 2026 13:46:06 GMT
• Title: Neutralizing Structural Inequality in the Nigerian FinTech Sector
• Abstract summary: This paper introduces a hierarchical human-AI triage model for Point of Sale fraud detection in the Nigerian FinTech sector.<n>Adopting a We Are All Equal worldview, we address the challenge of discrimination laundering.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic decision systems in financial services often rely on data proxies that inadvertently encode structural inequalities. This paper introduces a hierarchical human-AI triage model for Point of Sale fraud detection in the Nigerian FinTech sector. Adopting a We Are All Equal worldview, we address the challenge of discrimination laundering, wherein the system misinterprets infrastructure related aleatoric noise such as rural network timeouts as fraudulent intent. We implement a three-tier routing policy utilizing a calibrated ensemble model as a primary filter. The policy routes transactions characterized by epistemic uncertainty such as cold start new accounts to specialist analysts while reserving high stakes cases for a senior supervisor. To manage finite human capacity, we utilize a dynamic shadow price to ration human attention and implement a random audit mechanism to prevent human skill atrophy. Our experimental results demonstrate a statistically significant 1.88\% complementarity gap and a 24.79\% percentage point gain in fraud recall over an autonomous baseline. Crucially, the model reduces the regional performance gap from 19.43 to 2.88 percentage points, neutralizing structural bias. Hierarchical collaboration provides a robust mechanism for substantive equality of opportunity, ensuring that rural accounts are not excluded from the digital economy due to environmental brute luck.

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