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DDIF 2020 : ICDM 2020 Workshop on Deep Data Intelligence for Finance

Sorrento, Italy
Event Date: Nov 17, 2020 - Nov 20, 2020
Submission Deadline: Aug 24, 2020
Notification of Acceptance: Sep 17, 2020
Camera Ready Version Due : Sep 24, 2020

About

[DDIF: Deep Data Intelligence for Finance - 2020]

Financial Technology (FinTech) has seen billions of dollars of investments since 2010. With the evolving relationship with banking customers, financial institutions are eager to accelerate and harness the knowledge of new technologies and business models into launching competitive products and services. Artificial Intelligence (AI), especially the recent surge of interest in Deep Learning and Machine Learning technologies, has attracted attention from both academia and industry. The use of AI has been widely adopted in different disciplines such as information retrieval, natural language processing, and machine learning, to name a few. Notably, such technologies perform best when the training data is huge, and the abundance of many multi‑years of organizations’ financial data appears to be a good candidate for utilization. However, the use of AI technologies to unravel deep knowledge in the financial domain still appears to be limited, and works related to it have been scattered in different venues.

Call For Paper

his workshop aims to provide a forum for researchers and practitioners to present and discuss new ideas, trends and results concerning the application of AI-based methods to the Financial domain, especially in the area of mining financial data intelligence.

Topics include, but not limited to:

  • Applications of machine learning to financial software analysis
  • Natural language processing for analysing financial data
  • -Human-machine collaboration for financial software systems
  • Prediction models to support software quality and performance evaluation
  • Analysis of financial software repositories and multi-source financial data,
  • including risk data analysis
  • Mining financial data
  • Applications of machine learning in algorithmic trading

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