Utilizing the latest advancements in artificial intelligence (AI), natural language processing (NLP), semantic and other technologies, Recognos ETI allows companies to 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.”

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.

Key Features of the Recognos ETI platform include:
• Customizable Extraction Taxonomy
o Can be applied to any document set that has similar content, regardless of format
o 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

“Financial institutions and large corporations continue to spend billions of dollars to manually process important documents, including key AML/KYC compliance information that has become vital for these companies to maintain in light of increasing regulatory scrutiny,” said Ben Knieff, Senior Analyst, Aite Group. “Semantic technology platforms have the potential to significantly reduce costs and mitigate the risks associated with manual processing, as well as improve the overall quality of reference data.”