AI in risk management: An analytical comparison between the U.S. and Nigerian banking sectors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it