The ACM International Conference on AI in Finance (ICAIF) is a scholarly peer-reviewed conference that aims to bring together researchers from both academia and industry to share challenges, advances, and insights on the impact of Artificial Intelligence and Machine Learning on finance. ICAIF is supported by the Association for Computing Machinery (ACM). We invite participation from academia, government, regulatory agencies, financial institutions, NGOs, and other stakeholders in the AI and Finance community. ICAIF’24 invites high-quality research paper submissions that connect AI and Finance from both methodological and applicational perspectives.

ICAIF’24 will be hosted by the Finance and Risk Engineering Department, New York University’s Tandon School of Engineering, at the Brooklyn NY campus between 14-16 November 2024.

General Chairs 

• Dhagash Mehta (BlackRock)
• Guiling “Grace” Wang (New Jersey Institute of Technology)

Program Chairs

• Senthil Kumar (Capital One)
• Hao Ni (UCL)

Workshop Chairs

• Bo An (Nanyang Technical University)
• Yongjae Lee (Ulsan National Institute of Science and Technology)
• Zhen Zeng (JP Morgan)

Important Dates

AOE = Anywhere on Earth

• Paper submission deadline: July 15, 2024, 23:59 AOE
• Author notification: September 9 – September 13, 2024, 23:59 AOE
• Workshops: TBC (see accepted workshops page for details)
• Main conference: November 14 -16, 2024

Topic Areas

ICAIF ’24 invites high-quality research paper submissions that connect AI and Finance from both methodological and applicational perspectives.

Methodologies should be relevant to general financial problems that may include but are not limited to:

• Generative models and data-driven simulation
• Large Language Models
• Reinforcement learning and federated learning
• Meta learning and transfer learning
• Representation learning, natural language processing, and time series prediction
• Validation and calibration of financial models
• Multi-agent systems and game-theoretic analysis of financial markets
• Explainability, ethics, and fairness of AI & ML systems
• Security, and privacy of AI & ML systems
• Computational regulation and compliance in finance
• Robustness and uncertainty quantification
• Quantum computing
• Graph theory and network analysis
• Federated learning and decentralized finance

Potential applications of interest may include but are not limited to:

• Fraud detection for credit cards and mortgages
• Early detection of firm defaults
• Blockchain and cryptocurrency
• Risk modelling and risk management
• Trading (for example, optimal execution, market making, smart order routing and hedging)
• Asset pricing
• Robot-advising and investment recommendations
• Forecasting of financial scenarios
• Financial time series analysis and factor models
• Understanding customer behavior for credit decisioning, servicing and recommendations
• AI-Driven Financial Inclusion 
• Environmental, Social, and Governance (ESG) Investing

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