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Financial Data Science is an interdisciplinary field that leverages computing and pattern recognition techniques to analyze complex financial data. This session aims to explore the latest advancements in machine learning, deep learning, and data mining as applied to finance. Topics of interest include, but are not limited to, algorithmic trading, risk management, fraud detection, credit scoring, financial forecasting, and blockchain analytics. The session will provide a platform for researchers and practitioners to discuss innovative methodologies and applications that address challenges in financial data analysis.

 

Organizer: Asst. Prof. Peng Liu, Singapore Management University, Singapore

Committee Member: Prof. Jingzhu Wu, Beijing Technology and Business University, China

 

The topics of interest include, but are not limited to:

1. Algorithmic Trading

Reinforcement learning-based trading strategies

Trade execution algorithms (VWAP, TWAP, dark pool optimization)

Multi-asset portfolio optimization and rebalancing algorithms

2. Risk Management

Market risk modeling (VaR, CVaR, Expected Shortfall)

Credit risk prediction and default probability modeling

Extreme event forecasting using deep learning

3. Fraud Detection

Real-time credit card fraud detection (online learning models)

Money laundering pattern recognition (graph neural network applications)

Explainable AI for fraud decision-making

4. Credit Scoring

Alternative data (non-traditional credit data) in credit assessment

Credit risk modeling for small and medium enterprises (SMEs)

Deep learning-based unstructured data analysis (e.g., text, transaction records)

5. Financial Forecasting

Stock/cryptocurrency price time-series forecasting

Multimodal data fusion (text, image, market data) for prediction

Interpretability and stability analysis of forecasting models

6. Blockchain Analytics

On-chain transaction tracking and address clustering (deanonymization)

Smart contract vulnerability detection (formal verification + ML)

NFT trading pattern analysis and valuation models

7. Applications of Machine Learning in Finance

Large language models (LLMs) for financial text analysis (earnings reports, news, research)

Generative AI (GANs, diffusion models) for synthetic financial data generation

Reinforcement learning in portfolio management

Submission Guideline

Please submit your manuscript via Electronic Submission System (account is needed). (Please remark the session number when you make the submission.)

 

Important Dates

• Submission of Full Papers: June 05, 2025
• Notification of Review Result of Papers from Special Sessions: July 25, 2025
• Registration Deadline: August 25, 2025