AI in Financial Modelling and Forecasting: Rethinking Pedagogy for Finance Students
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The accelerating integration of artificial intelligence into financial services has exposed a growing misalignment between the competencies finance graduates possess and those demanded by the industry. This paper investigates how AI-driven methodologies including machine learning, natural language processing, and deep learning are fundamentally reshaping financial modelling and forecasting, and what this transformation implies for higher education in finance. Drawing on recent empirical studies, industry reports, and theoretical frameworks in pedagogy and fin-tech, the paper makes three contributions such as (i) it surveys the evolving landscape of AI applications in financial forecasting, (ii) it critically evaluates the limitations of conventional finance curricula in preparing students for AI-augmented workplaces, and (iii) it proposes a structured pedagogical framework that integrates computational literacy, ethical reasoning, and domain expertise. The findings suggest that rethinking finance pedagogy is not merely a curricular update but a foundational shift in how financial knowledge is produced, validated, and applied. Institutions that embrace this shift are better positioned to produce graduates capable of functioning effectively in data-intensive, algorithmically driven financial environments.
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