Revisiting AI in Finance and Economics: The Generative Paradigm and Its Implications for Markets and Policy

The rapid emergence of Generative Artificial Intelligence marks a fundamental shift in how financial systems operate and how economic theories are tested and applied. Traditional AI and machine learning models in finance have largely focused on prediction and classification using historical datasets. Generative AI, powered by Large Language Models, diffusion models, and synthetic data frameworks, moves beyond prediction to creation, simulation, and reasoning. This paper revisits core AI and ML techniques in finance and economics through the lens of this generative paradigm.

The study explores implications across three critical domains. First, in financial markets, generative AI is enabling dynamic scenario generation, synthetic data augmentation for risk models, and natural language-driven trading and advisory systems. These capabilities allow institutions to model tail risks and market shocks that conventional models fail to capture. Second, in economic policy and research, generative AI facilitates agent-based policy simulations, real-time narrative economic forecasting, and automated synthesis of vast economic literature. This supports central banks and policymakers in testing interventions and communicating outcomes with greater clarity. Third, in institutional decision-making, generative tools are democratizing access to complex financial analysis by transforming data into actionable insights for both experts and non-experts.

However, the generative turn also raises pressing questions on model reliability, interpretability, data privacy, and regulatory oversight. The paper proposes a framework for responsible adoption based on explainable AI, human-in-the-loop validation, and domain-specific governance.

Using case studies from global banks, fintech platforms, and policy think tanks, this paper argues that generative AI represents not just a technological upgrade but a structural reorientation of finance and economics. It concludes with recommendations for academia, industry, and regulators to revisit existing AI/ML techniques to build more adaptive, transparent, and inclusive financial systems.