DDIF: Deep Data Intelligence for Finance
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.
This 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
Authors are invited to submit original papers, which have not been published elsewhere and are not currently under consideration for another journal, conference or workshop.
Paper submissions should be limited to a maximum of eight pages (free of charge) and 10 pages (including a charge for two pages), in the IEEE 2-column format (https://www.ieee.org/conferences/publishing/templates.html), including the bibliography and any possible appendices.
Submissions longer than ten pages will be rejected without review. Authors are recommended to stick to the eight pages limit.
The authors shall omit their names from the submission. For formatting templates with author and institution information, simply replace all these information items in the template by "Anonymous".
In the submission, the authors should refer to their own prior work like the prior work of any other author, and include all relevant citations. This can be done either by referring to their prior work in the third person or referencing papers generically. For example, if your name is Smith and you have worked on clustering, instead of saying "We extend our earlier work on distance-based clustering (Smith 2005)", you might say "We extend Smith's earlier work (Smith 2005) on distance-based clustering". The authors shall exclude citations to their own work which is not fundamental to understanding the paper, including prior versions (e.g., technical reports, unpublished internal documents) of the submitted paper. Hence, do not write: "In our previous work " as it reveals that citation 3 is written by the current authors. The authors shall remove mention of funding sources, personal acknowledgments, and other such auxiliary information that could be related to their identities. These can be reinstituted in the camera-ready copy once the paper is accepted for publication. The authors shall make statements on well-known or unique systems that identify an author, as vague in respect to identifying the authors as possible. The submitted files shall be named with care to ensure that author anonymity is not compromised by the file names. For example, do not name your submission "Smith.pdf", instead give it a name that is descriptive of the title of your paper, such as "ANewApproachtoClustering.pdf" (or a shorter version of the same).
Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. Authors are strongly encouraged to make their code and data publicly available whenever possible. In addition, authors are strongly encouraged to also report, whenever possible, results for their methods on publicly available datasets.
All submissions will be reviewed by at least three reviewers. At least one author of each accepted paper must complete the conference registration and present the paper at the conference, in order for the paper to be included in the proceedings and conference program.
Paper Submission Link:
- Workshop paper submissions: August 24, 2020
- Workshop paper notification: September 17, 2020
- Camera-ready deadline and copyright forms: September 24, 2020
- Conference dates: November 17-20, 2020