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Financial Knowledge Graph & Beyond

Reason over big financial data and produce results with explanations

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. 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 acquirer recommendation, market research report generation, etc.

FINANCIAL KNOWLEDGE GRAPH

Sources of Data
  • 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;
Entities & Relations
We model relations between millions of companies, tens of thousands of investors,millions of persons and a lot other entities like stocks. Data from different sources is processed and integrated using the state of the art natural language processing and machine learning techniques.
NLP & Machine Learning Modules
A set of natural language processing and machine learning modules have been developed and integrated in our system, covering search, scoring, reasoning, ranking, etc. We also provide interfaces for directly calling such modules, for example, ‘find investors with similar investment patterns as Bridgewater Associates’.
Solutions
Finance problems are tricky. To deal with such problems, we build pipelines by assembling related modules and reasoning over the data from the “right” part of the knowledge graph. Different from black box AI solutions, our results come with human-readable explanations.

USE CASES

M&A Service for Corporations

Recommend potential acquirers and screen acquisition targets.

Learn More

Market Analysis for Startups

Position small businesses in merger & acquisition market.

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Corporate R&D Analysis

Analyze companies from the perspective of research & development.

See Example