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This paper introduces a hierarchical human-AI triage model for POS fraud detection in Nigerian FinTech, designed to neutralize structural bias that discriminates against rural agents due to infrastructure-related noise. The system uses a calibrated ensemble model, specialist analysts, and senior supervisors to achieve substantive equality of opportunity, reducing the regional performance gap from 19.43 to 2.88 percentage points.
This paper introduces the Nigeria Machinery Usage and Failures Dataset, 89 records across 28 indicators for Nigeria's manufacturing and oil/gas sectors from 2006 to 2025, along with a method to build domain-grounded chain-of-thought reasoning examples from sparse numeric values.
This paper applies Random Forest Recursive Feature Elimination to Nigerian household survey data to identify minimal predictors that accurately classify poverty status, quintile distribution, and inequality position, showing that machine learning can reduce data requirements while preserving distributional information for monitoring poverty and inequality.