KDF2020: The AAAI-20 Workshop on Knowledge Discovery from Unstructured Data in Financial Services

Країна: США

Місто: New York

Тези до: 15.11.2019

Дати: 07.02.20 — 08.02.20

Область наук: Економічні;

Е-мейл Оргкомітету: xiaomo.liu@spglobal.com; grace.bang@spglobal.com

Організатори: International Committee

 

Knowledge discovery from various data sources has gained the attention of many practitioners over the past decades. Its capabilities have expanded from processing structured data (e.g. DB transactions) to unstructured data (e.g. text images, and videos). In spite of major research focusing on extraction from news, web, and social media data, its application to data in professional settings such as legal documents, financial filings, and government reports, still present huge challenges. One reason is that the precision and recall requirements for extracted knowledge to be used in business process are fastidious.   

In the financial services industry in particular, a large amount of financial analysts’ work requires knowledge extraction from different data sources, such as SEC filings, loan documents, industry reports etc., before the analysts can conduct any analysis. This manual 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. These challenges and issues call for robust artificial intelligence (AI) algorithms and systems to help. The automated processing of unstructured data to discover knowledge from complex financial documents requires a series of techniques such as linguistic processing, semantic analysis, and knowledge representation and reasoning. The design and implementation of these AI techniques to meet financial business operations requires the joint effort between academia researchers and industry practitioners.  

Furthermore, alternative data like social media feeds and news are gaining traction as promising knowledge sources for financial institutions as they provide additional perspectives to the financial analysts when they make investment decisions. However, the volume of alternative data is usually vast and the valuable knowledge is always comingled with noise. 

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:

  • Natural language processing, understanding and generation from financial documents;
  • Search and question answering systems designed for financial corpora;
  • Named-entity disambiguation, recognition, relationship discovery, ontology learning and extraction in 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 data extraction from financial fillings and quality verification; 
  • Event discovery from alternative data and impact on organization equity price;
  • AI systems for relationship extraction and risk assessment from legal documents.  

Although textual data is prevalent in a large amount of finance-related business problems, 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 etc.

Веб-сторінка конференції: https://easychair.org/cfp/aaai-kdf-2020