The Trusted Location Intelligence Framework (TLIF): Integrating Digital Public Infrastructure into AI-Driven Tax Risk Profiling in Nigeria

The digital transformation of Nigeria's public sector has accelerated investments in Digital Public Infrastructure (DPI) to improve public service delivery, strengthen institutional interoperability, and enable data-driven governance. Within tax administration, advances in Artificial Intelligence (AI), enterprise analytics, and administrative data integration have enhanced the ability of revenue authorities to identify compliance risks and optimise enforcement activities. However, existing approaches to tax risk profiling continue to rely predominantly on financial, transactional, and behavioural indicators, with limited consideration of trusted digital location as a strategic analytical input. This paper proposes the Trusted Location Intelligence Framework (TLIF), a conceptual framework that integrates trusted digital location with key components of Nigeria's Digital Public Infrastructure, including digital identity, business registration, administrative data exchange, enterprise analytics, and AI-driven risk assessment. The framework demonstrates how trusted location intelligence can strengthen taxpayer verification, improve geographic risk analysis, enhance data interoperability, and support more evidence-based compliance interventions. By positioning trusted digital location as a foundational analytical layer within tax risk profiling, the framework extends current thinking on digital tax administration and provides a structured foundation for future empirical research and policy development in Nigeria. Keywords: Digital Public Infrastructure; Artificial Intelligence; Tax Administration; Tax Risk Profiling; Trusted Location Intelligence; Enterprise Analytics; Nigeria.