Neutralizing Structural Inequality in the Nigerian FinTech Sector
2026-07-11 • Computation and Language
Computation and LanguageArtificial IntelligenceComputers and SocietyHuman-Computer Interaction
AI summaryⓘ
The authors developed a method to better detect fraud in Nigeria’s digital payments by combining AI and human decision-making in three levels. Their system carefully decides when to involve specialists or supervisors, especially for tricky or important cases, helping avoid mistakes caused by poor internet connections in rural areas being wrongly seen as fraud. By balancing AI with human input and using smart rules to manage workload, they improved fraud detection accuracy and reduced bias between regions. This approach helps make sure people in rural areas have fair access to financial technology.
Algorithmic decision systemsPoint of Sale fraud detectionHierarchical human-AI triageStructural inequalitiesEpistemic uncertaintyAleatoric noiseCalibrated ensemble modelBias mitigationDynamic shadow priceFraud recall
Authors
Muhammad Abdullahi Said
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.