The rapid proliferation of emerging technologies, such as artificial intelligence, social networks, mobile applications, distributed ledger technologies, and cloud computing, has transformed payment, transfer, and investment processes by introducing a plethora of innovative tools. These tools generate vast amounts of data characterized by remarkable volume, velocity, and variability. Extracting valuable insights from this data presents significant research challenges and opportunities for financial decision-making.

Data mining has been widely applied across the financial industry and demonstrated its effectiveness and profitability in various applications, including credit risk analysis, portfolio optimization, market basket analysis, algorithmic trading, and financial time series forecasting.

The advent of large-scale machine learning models, such as GPT-4, has significantly impacted the field of data mining. The ability to process massive volumes of data in near real-time makes these models highly promising for the finance industry, where accurate and timely predictions are crucial to gain a competitive edge. However, the deployment of large-scale models in finance also raises concerns related to data privacy, security, and regulatory compliance. It is essential to address these challenges and develop robust frameworks for the responsible use of large-scale models in data mining, ensuring that the potential benefits are harnessed while minimizing the risks.

Moreover, the lucrative potential and pervasive role of finance in society has attracted malicious individuals who exploit financial data to commit crimes such as fraud and market distortion, making the fight against financial crime more difficult than ever. Furthermore, existing unfairness and biases in real-world data can inadvertently be incorporated into financial decision-making algorithms. There is mounting evidence that these algorithms, informed by data with ethical implications, may unintentionally produce discriminatory predictions. Thus, developing reliable and trustworthy data mining systems is a crucial task in finance.

The DMF 2023 workshop aims to gather researchers, practitioners, and developers from academia and the financial industry to discuss cutting-edge problems, potential solutions, and future directions. This collaboration will foster interactions among experts in these areas and promote the development of new interdisciplinary technologies.

Submission Guidelines

Authors are encouraged to submit original, English-language research contributions that have not been concurrently submitted or published elsewhere. All submissions must adhere to the IEEE 2-column format. For the regular paper track, submissions should not exceed 8 pages of content, plus an additional 2 pages for references. For the short paper track, submissions should be limited to a maximum of 4 pages of content, plus 1 extra page for references.

DMF 2023 runs two paper submission tracks: regular papers and short papers. The regular paper track welcomes submissions that emphasize theoretical foundations, algorithms, models, systems, and applications related to data mining in finance. Original research papers are particularly encouraged for the regular paper track. The short paper track seeks papers focusing on applied research, industrial experience reports, demonstrations (e.g., showcasing toolkits for financial data mining), and vision papers that introduce challenges and open problems.

In alignment with the ICDM 2023 reviewing scheme, all submissions will undergo triple-blind reviews by the Program Committee, evaluating technical quality, relevance to the conference scope, originality, significance, and clarity. All accepted papers will be presented as posters, with a select few chosen for oral presentations. A best paper award will be conferred. Accepted papers will be published in the IEEE ICDM 2023 Workshop proceedings (published by IEEE and EI-indexed).

Papers should be submitted by September 15, 2023 CST.

List of Topics

• DMF 2023 features three major themes:
• Effective and efficient financial data mining
• Large-scale models in financial data mining, such as
  • Data mining with AI-Generated Content (AIGC) in finance
  • Pretraining and fine-tuning of large-scale financial models
  • Prompt engineering of large-scale financial models
  • Deployment and maintenance of large-scale financial data mining models
• Trustworthy data mining in finance, such as
  • Explainable financial data mining models
  • Fairness-aware data mining in finance
  • Privacy-preserved data mining, simulation, and sharing in finance

Within the scope of these themes, the workshop welcomes submissions on a variety of topics, including but not limited to:

• Text data mining on financial reports and documents
• Social media mining in finance
• Graph-based financial data mining
• Ranking and search in finance
• Short or long sequence time series forecasting
• Trend detection
• Optimal execution
• Risk modeling
• Market monitoring and auditing

Important Dates

• paper submission deadline: September 15, 2023
• Notification of acceptance: October 6, 2023
• Camera-ready deadline and copyright form: October 13, 2023
• Workshop day: December 4, 2023

**All times are at 11:59PM CST**

Contact

All questions about submissions should be emailed to dmf23org@gmail.com