Generative Artificial Intelligence in Finance: A Systematic Literature Review and a Research Agenda

This study presents a systematic literature review on the implications of the use of generative artificial intelligence (GAI) in finance. With the rapid advancement of GAI technologies and hybrid adversarial‑variational frameworks, GAI’s integration into the financial industry has gained significant importance. Despite the growing body of research, comprehensive analyses of GAI’s potential applications, opportunities, and challenges in finance remain limited. The objective of our study is to synthesize the existing literature on the implications of GAI in finance and propose future research directions. The methodology involves a five‑step systematic literature review process, including identification, selection, relevance and quality assessment, data extraction, and data synthesis of relevant articles published between 2020 and 2025. The evaluation based on 42 selected articles highlights several applications of GAI in finance, which include synthetic data‑driven financial innovation, time‑series forecasting and algorithmic trading, risk modeling and stress testing, as well as GAI‑driven budgeting tools. Potential opportunities for GAI use in finance embrace enhanced operational efficiency, optimized customer service, innovation and sustainability capabilities, strengthened financial compliance, and improved data processing and analytical capabilities. Nevertheless, challenges such as technical risks, regulatory risks, ethics and moral concerns, market risks, operation and maintenance risks, and mental risks are also identified. Finally, we propose a research agenda focusing on both process‑related and content‑related recommendations to address these challenges and guide future research on the implications of GAI in finance.