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.
Important Dates (GMT)
|AAAI-20 Early Registration Deadline||Friday||December 13th, 2019|
|AAAI-20 Late Registration Deadline||Friday||January 10th, 2020|
|Workshop||Friday||Februray 7th, 2020|
For general inquiries about the KDF workshop and participation, please write to firstname.lastname@example.org.