KDF 2021 - The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial Services

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 substantial research focusing on discovery 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. Possible reasons are that the precision and recall requirements for extracted knowledge to be used in business processes are fastidious, and signals gathered from these knowledge discovery tasks are usually very sparse and thus the generation of supervision signals is quite challenging.
In the financial services industry, in particular, a large amount of financial analysts’ work requires knowledge discovery and 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 inefficient, 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 a joint effort between academia researchers and industry practitioners.
We invite submissions of original contributions on methods, applications, and systems on artificial intelligence, machine learning, and data analytics, with a focus on knowledge discovery and extraction in the financial services domain. The scope of the workshop includes, but is not limited to, the following areas:
Representation learning, distributed representations learning and encoding in natural language processing for financial documents;
Synthetic or genuine financial datasets and benchmarking baseline models;
Transfer learning application on financial data, knowledge distillation as a method for compression of pre-trained models or adaptation to financial datasets;
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;
Using multi-modal data in knowledge discovery for financial applications
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;
Accounting for Black-Swan events in knowledge discovery methods