PREDICTIVE ANALYTICS FOR FORECASTING IRREGULAR MIGRATION PATTERNS IN WEST AFRICA

This article examines the strategic relevance of Predictive Analytics (PA) in forecasting irregular migration patterns within West Africa. Drawing on the author’s dual expertiseover seven years as an Immigration Officer with the Nigeria Immigration Service (NIS) and current postgraduate program in Information Technology, the paper argues that current reactive border management strategies based on lagging indicators are no longer sufficient. By integrating Machine Learning (ML) models with multi-dimensional data sources—including socio-economic indicators, operational border records, conflict data, and environmental metrics. PA provides a proactive forecasting framework. The analysis demonstrates that PA enables dynamic resource allocation, early policy warning systems, and actionable intelligence for disrupting human smuggling networks. The paper concludes that PA is not optional but a strategic imperative for transforming West Africa’s border security architecture from reactive response to future-driven migration intelligence.