PROBLEM TO ADDRESS: It is a daily job for finance practitioners to collect and reason over a lot of data to make swift decisions. For example, listing potential acquirers for a specific business, or screening acquisition targets in accordance to certain criteria. Such work could usually take long hours to finish and involve reviewing information from multiple sources.
MISSION: FMeasure is dedicated to making this process automatic, and producing results with human-readable explanations in seconds.
SOLUTION: We track and analyze financial data in real-time and integrate all such data in a large financial knowledge graph (KG). Leveraging this knowledge graph and a set of built-in natural language processing and machine learning components, we are able to quickly assemble solutions to tricky problems in financial industry.
USE CASES: In addition to handling knowledge graph queries, we are also able to deliver results with explanations for a number of tricky financial tasks like technology landscape analysis, acquirer recommendation, market research report generation, etc.
FINANCIAL KNOWLEDGE GRAPH (KG)
- financial statements, stock prices;
- filings, earning call transcripts, news;
- M&A transactions, funding rounds, insider/institutional investor activities;
- meta data covering both public and private companies;
- public employee information;
- patents, trademarks, and papers;