Calls for Papers
Generative AI in Finance: Innovation, Risk, and Systemic Stability
This special collection focuses on the rapidly growing role of generative artificial intelligence (AI) in transforming financial markets, institutions, and decision-making processes. Recent advances in large language models (LLM), generative neural networks, and foundation models have enabled new forms of data generation, prediction, and automation that extend well beyond traditional machine learning approaches. These developments are reshaping financial modeling, risk management, trading, regulatory compliance, and customer-facing financial services.
The objective of this collection is to bring together theoretical, empirical, and applied research that examines both the opportunities and challenges posed by generative AI in finance. We invite high-quality submissions addressing, but not limited to, the following themes.
Topics of Interest
- Applications of generative AI in financial modeling, forecasting, and risk management, including asset pricing, portfolio optimization, and stress testing
- Effects of generative AI on financial markets and institutions, such as trading behavior, liquidity, price discovery, and organizational decision-making
- Case studies and real-world implementations of generative AI in banking, asset management, insurance, fintech, and regulatory technology (RegTech)
- Macroeconomic and financial stability implications of generative AI, including effects on aggregate risk, credit cycles, asset price dynamics, and systemic resilience
- Future directions and emerging trends in AI-driven financial technologies, governance structures, and human-AI collaboration in finance
This collection seeks to foster interdisciplinary dialogue among researchers in finance, economics, computer science, and policy, and to advance both academic understanding and practical insights into the evolving role of generative AI in the financial ecosystem.
Guest Editors
Raffaella Calabrese
University of Edinburgh, United Kingdom
About
Raffaella is Professor in Statistics and Data Science at the University of Edinburgh Business School. She is an expert in Climate Stress Testing, Open Banking, FinTech, credit risk modelling and access to finance for small businesses. She develops new analytical techniques for credit risk, such as scoring models, Loss Given Default, stress testing, interpretability, fairness and explainability. She collaborates with financial institutions, government bodies and regulators, across different countries, including the UK, the US, China and multiple EU countries.
Xinheng Liu
Changsha University of Science and Technology, China
About
Xinheng Liu is an Associate Professor at Changsha University of Science & Technology. He obtained his Ph.D. in Finance from Sun Yat-sen University in 2022. His research focuses on corporate finance, climate finance, and green finance. He has published extensively in leading academic journals, including the International Journal of Finance & Economics, Energy Economics, Economics Letters, and International Review of Economics & Finance.
Jörg Osterrieder
University of Twente, The Netherlands
About
Jörg Robert Osterrieder is Associate Professor of Finance and Artificial Intelligence at the University of Twente and Professor of Finance at the University of Applied Sciences of the Grisons. His research spans artificial intelligence, machine learning, quantitative finance, risk management, and digital finance. He coordinates the €3.8 million EU-funded Marie Skłodowska-Curie Industrial Doctoral Network on Digital Finance and previously chaired the European COST Action on FinTech and AI in Finance.
Claudia Tarantola
University of Milan, Italy
About
Claudia Tarantola is Full Professor of Statistics in the Department of Economics, Management, and Quantitative Methods at the University of Milan. Her research spans Bayesian statistics, clustering techniques, copula modeling, categorical data analysis, graphical models, and statistical approaches to financial risk. She served as Italy's representative on the Management Committee of the COST Action CA19130 FinTech and Artificial Intelligence (2020-2024).
Ruting Wang
City University of Hong Kong, Hong Kong
About
Ruting Wang is a postdoctoral researcher at City University of Hong Kong and Sun Yat-sen University. She is a member of the IDA Institute of Digital Assets, Bucharest University of Economic Studies and MSCA Digital Finance. Her research fields include Digital Assets, Corporate Finance, Quantitative Finance, Financial Intermediation, Risk Management, Green Finance, and Causal Inference.