The Impact of Artificial Intelligence-Enabled Recruitment on Hiring Quality and Diversity Outcomes in Nigerian Financial Institutions

This study investigates the impact of artificial intelligence (AI)-enabled recruitment tools on hiring quality and workforce diversity outcomes in Nigerian financial institutions, addressing an emerging and empirically underexplored area of HR technology adoption in Sub-Saharan Africa. Several Nigerian banks and fintech firms have adopted AI-assisted CV screening, video interview analysis, and predictive candidate assessment tools in their talent acquisition processes. The study employs a comparative mixed-methods design, contrasting hiring quality and diversity metrics between 10 AI-adopting and 10 non-adopting financial institutions. Primary data are collected through interviews with 25 talent acquisition heads and secondary data through hiring outcome records provided by participating institutions, including quality of hire ratings, attrition within first year, and diversity demographic profiles. The Technology Acceptance Model and Algorithmic Accountability Framework provide the theoretical grounding. Findings reveal that AI-adopting firms demonstrate higher average quality-of-hire scores and shorter time-to-fill metrics. However, the study also finds troubling evidence that AI screening tools trained on historical hiring data perpetuate existing demographic biases, with female candidates and applicants from certain geopolitical zones receiving lower algorithmic scores independent of qualification parity. Recommendations include mandatory bias audits for AI recruitment tools, human oversight checkpoints, and diversity-weighted candidate pool requirements. This research contributes a critically important governance dimension to the emerging literature on AI in Nigerian HRM. Keywords: Artificial Intelligence, Recruitment, Hiring Quality, Diversity, Financial Institutions.