AI in risk management: An analytical comparison between the U.S. and Nigerian banking sectors
1 Independent Consultant, Nigeria.
2 Independent Researcher, Hamilton, Ontario, Canada.
3 Independent Researcher, Canada.
4 Christfill Global Enterprises, Lagos Nigeria.
5 Gerald and Gerald Exchanges, Lagos, Nigeria.
6 University of Bedfordshire, UK.
Review
International Journal of Science and Technology Research Archive, 2024, 06(01), 127–146.
Article DOI: 10.53771/ijstra.2024.6.1.0035
Publication history:
Received on 13 February 2024; revised on 20 March 2024; accepted on 23 March 2024
Abstract:
This paper presents a comprehensive review of the application and impact of Artificial Intelligence (AI) in risk management within the banking sectors of the United States and Nigeria, emphasizing a comparative analysis. The objective is to assess how AI technologies are adopted and implemented in risk management practices across these diverse banking environments, identifying the benefits achieved and the challenges faced.
The review synthesizes existing literature, including case studies, industry reports, and academic research, to outline the current state of AI in risk management. It delves into various risk types such as credit, market, operational, and compliance risks, exploring the specific AI tools and techniques employed to address these risks in each country.
Key findings suggest that U.S. banks have a more mature implementation of AI in risk management, characterized by the adoption of advanced analytics, machine learning models, and natural language processing for enhanced decision-making, fraud detection, and compliance monitoring. In contrast, the Nigerian banking sector is at a nascent stage of AI adoption, with efforts hampered by challenges like inadequate technological infrastructure, regulatory hurdles, and a lack of skilled professionals in AI.
Despite these differences, the paper identifies a strong interest and potential for growth in AI applications within the Nigerian banking sector, spurred by an increasing recognition of AI's value in enhancing competitiveness and meeting regulatory demands.
Conclusively, the review underscores the critical role of supportive regulatory policies, investment in technological infrastructure, and capacity building in human capital as pivotal elements for fostering the effective integration of AI in risk management. The comparative analysis reveals both the disparities and potential areas of collaboration between the U.S. and Nigerian banking sectors, advocating for a global dialogue on best practices and strategies for AI adoption in risk management.
Keywords:
Artificial Intelligence; Banking; Risk management; Machine learning; Predictive analytics; Natural language processing; Technological barriers; Infrastructural challenges; Ethical AI; Quantum computing; Blockchain;
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0