Kanu, Anthonia D (2026) The role of Generative Artificial Intelligence (GEN AI) in risk forecasting for investment management in the Nigerian financial institutions. Masters thesis, York St John University.

Text (The role of Generative Artificial Intelligence (GEN AI) in risk forecasting for investment management in the Nigerian financial institutions) THE ROLE OF GENERATIVE ARTIFICIAL INTELLIGENCE (GEN-AI)IN RISK FORECASTING FOR INVESTMENT MANAGEMENT IN NIGERIAN FINANCIAL INSTITUTIONS.docx - Published Version Available under License Creative Commons Attribution Share Alike. Download (165kB)

Abstract

This study critically investigates the role of Generative Artificial Intelligence (Gen-AI) in enhancing investment risk forecasting within Nigerian financial institutions. While AI adoption in financial services is accelerating globally, there remains a significant gap in empirical research examining whether Gen-AI meaningfully improves forecasting performance in emerging markets characterised by volatility, weak regulatory frameworks, and digital inequality. Unlike traditional predictive models reliant on historical data, Gen-AI enables scenario simulation, unstructured data processing, and synthetic data generation offering potentially superior forecasting capabilities.

Grounded in a positivist paradigm, the study employs a quantitative, cross-sectional survey design targeting 336 professionals across Nigerian financial institutions. Data were collected using a structured questionnaire and analysed using descriptive statistics, Exploratory Factor Analysis (EFA), and regression techniques. The findings reveal that while Gen-AI adoption is gradually expanding, it does not independently predict improved forecasting performance. Instead, institutional readiness and ethical governance emerged as more robust predictors, highlighting that socio-technical integration, not adoption alone, drives Gen-AI effectiveness

Theoretically, the research affirms the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and the Resource-Based View (RBV) by demonstrating that adoption is a necessary but insufficient condition for AI success. Empirically, it offers one of the first evidence-based evaluations of Gen-AI’s role in investment risk forecasting in sub-Saharan Africa, shifting the focus from adoption metrics to performance-centred outcomes. Practically, it underscores the importance of explainability, stakeholder trust, and ethical frameworks for financial institutions and regulators.

While the study is limited by its cross-sectional design, modest R² value, and single country focus, it lays a foundation for future longitudinal and comparative research. It contributes to both AI innovation literature and the growing discourse on responsible AI governance in financial services, offering insights for technology strategists, policymakers, and institutional leaders navigating the intersection of Gen-AI and risk management.

Item Type:	Thesis (Masters)
Status:	Unpublished
Subjects:	H Social Sciences > HG Finance Q Science > Q Science (General) Q Science > Q Science (General) > Q325 Machine learning
School/Department:	York Business School
URI:	https://ray.yorksj.ac.uk/id/eprint/13991

University Staff: Request a correction | RaY Editors: Update this record