In the financial industry, data mining has been applied in almost every aspect and proven to be effective and profitable in practice. The DMF 2024 workshop intends to bring together researchers, practitioners, and developers from academia and financial industry to discuss cutting-edge problems, potential solutions, and future directions, such that the interactions among the expertise in these areas can be promoted and new interdisciplinary technologies can be developed.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

• 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. In alignment with the ICDM 2024 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 2024 Workshop proceedings (published by IEEE and EI-indexed).

List of Topics

DMF 2024 features three major themes:

• Effective and efficient financial data mining
• Trustworthy data mining in finance, such as
  • Explainable financial data mining models (e.g., key feature recognition, predictive performance analysis)
  • Fairness-aware financial data mining (e.g., fairly treating customer accounts, shareholder equity)
  • Privacy-preserved financial data mining, simulation, and sharing
• LLM-based financial data mining, such as
  • Data mining based on financial large language models
  • Training technology of financial large language models
  • Data cleaning and collecting pipelines of pre-training large models
  • Financial applications of LLMs (e.g., language models, vision models, audio models, and video models)
  • Model hallucination of large financial models
  • Application of large models under financial compliance constraints
  • Deployment of large financial models Inference acceleration and high-concurrency techniques for large financial models

Committees

Organizing committee

• Shuoling Liu is the Head of Institute of Innovation, Executive Chairman of FinTech Technology Committee, and Managing Director at E Fund. He is also a FinTech Committee member of Asset Management Association of China (AMAC) and a member of Quantitative Investment Committee of Investment Technology League (ITL). His research interest includes algorithmic trading, AI-based bond default prediction, and big-data-based ESG research. His research on bond default was awarded the second prize of the Annual FinTech Development by the People's Bank of China in 2020 and selected as the Capital Market FinTech Innovation Pilot Project in Guangzhou. He holds more than 10 FinTech-related patents.
• Liyuan Chen is the Chief Operating Officer and Chairman of FinTech Technology Committee of E Fund. She is a senior executive with more than 10 years of experience in business and technology. She has been committed to promoting the application of financial technology and made great contributions of the digital intelligence level of E Fund as well as the industry. She leads the FinTech Sector of E Fund, whose scientific researches has been transformed into high-profit productivities and publications in top-tier conferences.
• Haishan Wu is the vice general manager of AI Department at WeBank. He was the Director and AI scientist at BlackRock. He was the founder and CEO of SenSight.ai before joining BlackRock. Previously, he was the Chief Data Scientist of Heyi Ventures. He was also the consultant of Baidu Capital. His researches were not only published in prestigious journals and conferences, but also widely featured by major international media outlets. He was recognized as 35 Innovators Under 35 in China by MIT Technology Review in 2017. He earned his PhD degree from Fudan University in 2011 and was the Postdoc researcher in Princeton University in 2013-2014.
• Lixin Fan is currently the principal scientist of artificial intelligence with WeBank. He was the inventor of more than 100 patents filed in USA, Europe, and China. He was with Nokia Research Center and Xerox Research Center Europe. His research interests include the wellknown bag of keypoints image classification method, machine learning and deep learning, computer vision and pattern recognition, and image and video processing. He has participated in the NIPS/NeurIPS, ICML, CVPR, ICCV, ECCV, IJCAI and other top artificial intelligence conferences for a long time. He was the area chair of the AAAI and organized workshops in various technical fields. He is currently the chairman of the IEEE P2894 Explainable Artificial Intelligence Standard Working Group. He is a senior member of IEEE.
• Yue Cui is a Ph.D. student at the Hong Kong University of Science and Technology. She is interested in developing effective and trustworthy algorithms for data science and artificial intelligence (AI). She received the best paper runner-up award of APWeb-WAIM 2022. She has published over 15 peer-reviewed papers in top-tier conferences and journals in the area of data mining and trustworthy AI, including KDD, WWW, VLDB, ICDE, TOIS etc. She serves as the Program Committee member and reviewer for many top-tier conferences and journals, including ICML, KDD, ICDE, TKDE, CIKM etc.
• Qiang Yang is Chief Artificial Intelligence Officer of WeBank and Chair Professor of CSE Department of Hong Kong University of Science and Technology. He is the Conference Chair of AAAI-21, President of Hong Kong Society of Artificial Intelligence and Robotics (HKSAIR) , the President of Investment Technology League (ITL) and a former President of IJCAI (2017-2019). He is a fellow of AAAI, ACM, IEEE and AAAS. His research interests include transfer learning and federated learning. He is the founding EiC of two journals: IEEE Transactions on Big Data and ACM Transactions on Intelligent Systems and Technology.

Venue

The conference will be held in Abu Dhabi, UAE

Contact

All questions about submissions should be emailed to tangfangkai@efunds.com.cn