Artificial Intelligence and Corruption Reduction in Nigeria: A Study of the EFCC (2021–2026)

This study examines the role of artificial intelligence (AI) in reducing corruption within Nigeria's public sector, with a specific focus on the Economic and Financial Crimes Commission (EFCC) between 2021 and 2026. Corruption remains one of the most formidable obstacles to Nigeria's economic development, democratic consolidation, and institutional integrity, with billions of dollars lost annually to fraudulent practices across all levels of governance. Traditional anti-corruption methods including manual auditing, whistleblower policies, and investigative journalism have achieved limited success due to systemic weaknesses, political interference, and the increasing sophistication of financial crimes. This study argues that AI technologies offer transformative potential for enhancing corruption detection, investigation, and prevention through advanced data analytics, pattern recognition, predictive modelling, and automated transaction monitoring. Drawing on a qualitative case study design, including document analysis and expert interviews with EFCC officials, technology experts, and anti-corruption practitioners, the study assesses the deployment, effectiveness, and challenges of AI systems including the Eagle Eye platform, forensic accounting software, and machine learning algorithms for suspicious transaction detection. The study identifies significant achievements in asset tracing, financial forensics, and investigative efficiency, alongside persistent challenges including inadequate technical infrastructure, data quality issues, personnel skill gaps, legal framework weaknesses, and resistance from corrupt networks. The study acknowledges important limitations, including restricted access to EFCC operational records, the difficulty of isolating AI's specific contribution from other anti-corruption factors, and the rapidly evolving nature of AI technologies. The paper concludes with actionable recommendations for strengthening AI deployment through institutional capacity building, legal reform, regional cooperation, and sustainable funding.