New York-based Recognos Financial has announced the launch of Recognos ETI, a new data extraction platform “designed to unlock greater context and insight from otherwise untapped business data.” 

Recognos is a provider of artificial intelligence-based data management solutions for the financial services industry. The firm uses artificial intelligence (AI), natural language processing (NLP), semantic and other technologies to help enable companies extract, transform and integrate both structured and unstructured documents and data into useable business intelligence.  

"Recent studies have shown that 80% of all data in large organizations is unstructured, including many mission-critical documents such as contracts, prospectuses, and compliance documentation,” said Drew Warren, president and CEO of Recognos Financial. “Recognos ETI gives users the ability to go beyond traditional analytics to actively ask questions of their data to find the right information faster to improve business performance.”

Key features of the Recognos ETI platform include:

Customizable Extraction Taxonomy – It can be applied to any document set that has similar content, regardless of format; and it can define and store multiple  Extraction Taxonomies based on document type.

Ease of Use – Requires no technical background to deploy, and no additional programming to process new document types. 

Proprietary Machine Learning Table Extraction – Recognize, extract and transform data from tables based on your interactions, and record the information for future use.

Format Agnostic – Transform and export the combined data into any format for greater flexibility.

Language Agnostic - Designed to recognize most languages and translate data into the correct format without additional input. 

OCR Compatible – Able to recognize and transform scanned documents, and is compatible with most OCR scanning products.

  • Warren explains that -- using user-defined extraction criteria (the Extraction Taxonomy) -- the Recognos ETI platform leverages machine learning, natural language processing and semantic techniques to maximize the accuracy and quality of the data extracted. 

The platform also utilizes ‘human in the loop’ machine learning technology to continuously improve the data extraction process and minimize the extraction errors, Warren says.

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