Mission & Scope
SIG-AI in Finance is dedicated to advancing the application of artificial intelligence and machine learning in financial services. We bring together researchers from quantitative finance, computer science, and data science to explore how AI technologies can transform trading, risk management, credit assessment, and financial decision-making.
Our group emphasizes both the potential and the limitations of AI in finance, with particular attention to model interpretability, fairness, and regulatory compliance. We investigate cutting-edge techniques while maintaining a critical perspective on their real-world applicability.
Research Focus Areas
- Algorithmic Trading: Deep learning for price prediction, reinforcement learning for portfolio optimization, and high-frequency trading strategies
- Credit Scoring: Machine learning models for credit risk assessment, alternative data integration, and fair lending compliance
- Fraud Detection: Anomaly detection, network analysis for financial crime, and real-time transaction monitoring
- NLP for Finance: Sentiment analysis, news-based trading, financial document processing, and earnings call analysis
- Robo-Advisory: Automated portfolio management, personalized financial advice, and hybrid human-AI advisory models
- Risk Management: AI-enhanced stress testing, market risk modeling, and operational risk prediction
- Explainable AI (XAI): Interpretable models for regulatory compliance, model validation, and consumer trust
Current Research Themes
Active research topics within our SIG include:
- Large Language Models (LLMs) in financial analysis and advisory
- Graph neural networks for financial network analysis
- Federated learning for privacy-preserving financial AI
- Causal inference in financial machine learning
- AI model governance and regulatory sandboxes
- Bias detection and mitigation in financial AI systems
- Quantum machine learning for finance
Publications & Working Papers
SIG members publish in leading AI and finance venues including:
- Digital Finance (Springer)
- Journal of Financial Economics
- Journal of Machine Learning Research
- NeurIPS Workshop on AI for Finance
- ICAIF - ACM International Conference on AI in Finance
- Quantitative Finance
Working papers from SIG members will be listed here upon publication.
Recommended Reading
Foundational Papers
- Gu, S., Kelly, B., & Xiu, D. (2020). "Empirical Asset Pricing via Machine Learning." Review of Financial Studies, 33(5), 2223-2273. DOI
- Heaton, J.B., Polson, N.G., & Witte, J.H. (2017). "Deep Learning for Finance: Deep Portfolios." Applied Stochastic Models in Business and Industry, 33(1), 3-12. DOI
- Dixon, M., Halperin, I., & Bilokon, P. (2020). Machine Learning in Finance: From Theory to Practice. Springer.
- Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
Recent Research
- Chen, L., Pelger, M., & Zhu, J. (2024). "Deep Learning in Asset Pricing." Management Science, 70(2), 714-750. DOI
- Wu, S., Irsoy, O., Lu, S., et al. (2023). "BloombergGPT: A Large Language Model for Finance." arXiv preprint. arXiv
- Cao, S., Jiang, W., Yang, B., & Zhang, A.L. (2023). "From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses." Journal of Financial Economics, 160, 103910. DOI
- Bybee, L., Kelly, B., Manela, A., & Xiu, D. (2023). "Business News and Business Cycles." Journal of Finance, 78(5), 2765-2828. DOI
Survey Papers
- Ozbayoglu, A.M., Gudelek, M.U., & Sezer, O.B. (2020). "Deep Learning for Financial Applications: A Survey." Applied Soft Computing, 93, 106384. DOI
- Xing, F.Z., Cambria, E., & Welsch, R.E. (2018). "Natural Language Based Financial Forecasting: A Survey." Artificial Intelligence Review, 50(1), 49-73. DOI
How to Join
Membership in SIG-AI in Finance is open to all SDF members with research interests in artificial intelligence and machine learning applications in finance. To join:
- Become an SDF member (if not already)
- Express your interest by contacting the SIG leadership
- Participate in SIG activities and contribute to collaborative research