Intelligent Architecture for Comparative Analysis of Public Companies Using Semantics and XBRL Data

Abstract

"The new source of power is not money in the hands of a few, but information in the hands of many." The aforementioned quote from John Naisbitt seems to be even more relevant in the world of finance at this very moment. Many financial decisions come from watching the information stream, selecting relevant data, analyzing it and acting accordingly. With the increasing global competition, the need for swift data analysis, high accuracy and quality becomes a must. XBRL (Extensible Business Reporting Language)XBRL: http://www.xbrl.org/ standard was proposed to improve efficiency of data exchange in the financial domain. However; it is still struggling with interoperability problems, not to mention comparability of data or multisource data integration. This paper presents the FLORA intelligent platform: an approach for dealing with current financial information shortcomings and achieving more effective way of processing financial data based on the Linked Data principles. The article also explains the process of data extraction and semantic modeling which are the cornerstones of efficient financial data analysis. As a result, the FLORA architecture facilitates effective, data-driven, financial analyses and Web-scale integration between financial applications and platforms.