Abstract
There is a significant amount of "public" unstructured content available that is centered around the financial performance and counterparty relationships of a wide range of organizations, including private and public companies, governments, and public utilities. However, the wealth of structured entity and relationship information is buried inside a large amount of unstructured data. Converting such data into a structured format is essential for building novel analytics applications including counterparty and credit risk monitoring, investment decision making, development of new financial indices, systemic risk analysis, etc. In this paper, we illustrate three different application use cases where large-scale extraction and integration from the relevant public data enabled the creation of new applications to perform novel modeling and analysis. We discuss the main technological challenges encountered in such extraction and integration, and outline the common architectural platform used to build the three application use cases. Copyright 2014 ACM.