Generative AI in finance: Replicability, methodological contingencies, and future research directions

Abstract

• Maps the scholarly landscape of generative AI in finance. • Reveals six themes reshaping markets, decisions, and accountability. • Charts future directions on replicability, theory, and governance. Generative Artificial Intelligence (AI) is reshaping finance by transforming decision-making, risk management, and stakeholder engagement. This study provides a theory-informed synthesis of 84 peer-reviewed articles (2022–2025) using PRISMA-based screening, bibliometric analysis, and Structural Topic Modeling (STM). Six themes emerge: financial decision-making, ESG analytics, stock market prediction, advanced modeling for fraud detection and explainable AI, ChatGPT in accounting and education, and sentiment analysis with domain-specific LLMs. Findings show that generative AI enhances predictive capabilities and ESG assessments but raises issues of bias, transparency, and regulation. The review outlines future research priorities around interpretability, multimodal data, and governance frameworks.

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