Author
Abstract
This study investigates the influence of artificial intelligence on the effectiveness of document management within Nigerian banking institutions. In a context where conventional document management has transitioned to digital advancements, the incorporation of AI technologies, encompassing machine learning and natural language processing, promises enhanced accuracy, diminished turnaround times, and heightened overall efficiency. Employing a quantitative study design, data were gathered using questionnaires from a randomly selected sample of branches of Fidelity, GTB, Polaris, Stanbic, and Eco Bank, thereby assuring a robust representation of the 281 respondents. The research examined three hypotheses concerning the influence of AI on document classification, retrieval efficiency, and overall operational performance, with empirical evidence indicating substantial enhancements in these areas. Employing multiple regression analysis alongside statistical and computational methods, the findings indicate that AI integration achieves an average classification accuracy of 92.3%, markedly decreases document retrieval time, and exhibits a robust positive correlation with enhanced efficiency. The ramifications of these findings extend beyond mere enhancement of operational performance, offering significant insights for the banking sector and potentially other businesses facing analogous difficulties. This study advocates for the adoption of AI-driven document management systems, acknowledging the necessity for meticulous deployment to alleviate related risks, including data protection concerns. This study significantly contributes to the scientific discourse on AI integration in financial institutions and inspires future innovations in the field.
Suggested Citation
Osita Amaugo, 2025.
"Impact of Artificial Intelligence on Effective Document Management in the Banking Sector in Nigeria,"
International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(5), pages 4370-4380, May.
Handle:
RePEc:bcp:journl:v:9:y:2025:issue-5:p:4370-4380
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