Knowledge discovery from unstructured data has gained the attention of many practitioners over the past decades. In spite of major AI research focusing on data sources like news, web, and social media, its application to data in professional settings such as legal documents and financial filings, still present huge challenges.

In the financial services industry, in particular, vast analysis work requires knowledge discovery from various data sources, such as SEC filings, loan documents, and industry reports. The manual knowledge discovery and extraction process is usually low in efficiency, error-prone, and inconsistent. It is one of the key bottlenecks for financial services companies in improving their operating productivity. Furthermore, alternative data like social media feeds and news are gaining traction as promising knowledge sources for financial institutions as they provide additional perspectives when they make investment decisions. However, the valuable knowledge is always comingled with immense noise and the precision and recall requirements for extracted knowledge to be used in the business process are fastidious.

These challenges and issues call for the need of robust artificial intelligence (AI) algorithms and systems. The design and implementation of these AI techniques to meet financial business operations requires a joint effort between academic researchers and industry practitioners.

This one-day workshop will include invited speaks, paper presentations, and poster sessions to showcase research opportunities, novel solutions and systems, and success stories. We cordially welcome researchers, practitioners, and students from academic and industrial communities who are interested in the topics to participate and/or submit their original work.

List of Topics

We invite submissions of original contributions on methods, theory, applications, and systems on artificial intelligence, machine learning, natural language processing, big data, statistical learning, data analytics, and deep learning, with a focus on knowledge extraction in the financial services domain. The scope of the workshop includes, but is not limited to, the following areas:

• Knowledge representation;
• Natural language processing and understanding for financial documents;
• Search and question answering systems designed for financial corpora;
• Named-entity recognition, disambiguation, relationship discovery, ontology learning and extraction from financial documents;
• Knowledge alignment and integration from heterogeneous data;
• AI assisted data tagging and labeling;
• Data acquisition, augmentation, feature engineering, and analysis for investment and risk management;
• Automatic knowledge extraction from financial filings and quality verification;
• AI systems for relationship extraction and risk assessment from legal documents;
• Event discovery from alternative data and impact on organization equity price.

We also encourage submissions of studies or applications pertinent to finance using other types of unstructured data such as financial transactions, sensors, mobile devices, satellites, social media.

Submission Guidelines

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

• full-length (8 pages)
• Short/poster (2-4 pages) 

All submissions must be original contributions, following the AAAI-20 formatting guidelines. Submission will be accepted via EasyChair submission website.

Committees

Organizing committee

• Dr. Xiaomo Liu - Xiaomo is a Director of Data Science at S&P Global Ratings focusing on using machine learning and natural language processing to improve credit rating practices. Prior to S&P, Xiaomo was a senior research scientist at Thomson Reuters, where he invented AI algorithms and systems to help news and legal professionals to automate their workflow. Xiaomo holds a PhD in computer science from Virginia Tech and published over 30 papers and 3 patents.
• Dr. Sameena Shah - Sameena is a Managing Director and the Head of Data Science for S&P Global Ratings. Previously Sameena led AI strategy, Research and development for Financial and News verticals of Thomson Reuters, where Sameena and her team built and deployed several AI systems including Reuters Tracer, and Magnet. Sameena has worked or consulted for several hedge funds, Asset management firms and startups. She has won several best paper and industry awards, and holds a PhD and Masters in Machine Learning and Optimization from IIT Delhi. She served as an organizer for NIPS 2018 FEAP-AI4F workshop and invited speaker in KDD ADF 2019 workshop.
• Dr. Manuela M. Veloso - Manuela is on leave from Carnegie Mellon University (CMU) where she is Herbert A. Simon University Professor in the School of Computer Science, and where she was the Head of the Machine Learning Department until June 2018. Manuela recently joined J.P.Morgan Chase to create and head an Artificial Intelligence (AI) Research Center. She researches in AI, Robotics, and Machine Learning. At CMU, she founded and directs the CORAL research laboratory, for the study of autonomous agents that Collaborate, Observe, Reason, Act, and Learn, www.cs.cmu.edu/~coral. Veloso is AAAI Fellow, ACM Fellow, AAAS Fellow, and IEEE Fellow, Einstein Chair Professor of the Chinese National Academy of Science, the co-founder and past President of RoboCup, and past President of AAAI. Veloso and her students research a variety of autonomous robots, including mobile service robots and soccer robots.  See www.cs.cmu.edu/~mmv for further information, including publications. Manuela was the Program Chair for IJCAI-07 and Program Co-chair for AAAI-05. She also served as organizers for multiple workshops.
• Dr. Quanzhi Li – Quanzhi is a senior manager at Alibaba Group. His research interests are NLP, information retrieval, data mining, and machine learning. He has worked on many AI projects in financial domain during his tenure at Alibaba, Thomson Reuters, FactSet and Financial Times. He has published 50+ peer-reviewed papers and is the organizer of the yearly IEEE International Workshop on Big Data for Financial News and Data.
• Dr. Le Song – Le is a Sr. Director of AI at Ant Financial and Associate Professor, Computational Science and Engineering, Georgia Institute of Technology. Le is heading the machine learning group at Georgia Tech.  He developed core machine learning methodology, including kernel methods, feature space embedding methods, graphical models, probabilistic and stochastic modeling, deep learning models, etc. Le received his Ph.D. in Computer Science from the University of Sydney, Australia. He worked as a postdoc fellow at Carnegie Mellon University and a research scientist at Google Research before joining Georgia Tech. He has published more than 140 peer-reviewed papers and received multiple recognitions including best paper awards in NIPS 2013 and ICML 2010. He has organized six workshops in various machine learning conferences and gave tutorial talks in KDD, ICML, and WWW. 

Program Committee

• Armineh Nourbakhsh (S&P Global)
• Georg Kucsko (Kensho)
• Gianluca Demartini (University of Queensland)
• Grace Bang (S&P Global)
• Mingyang Di (S&P Global)
• Qian Yang (Duke University)
• Sumitra Ganesh (JP Morgan)
• Zhiqiang Ma (S&P Global)

Venue

The conference will be held in Hilton New York Midtown, New York, New York, USA.

Contact

All questions about the workshop and/or submissions should be emailed to kdf2020 at easychair.org