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Semantic Technology: A New Approach to Financial Data

By   /  December 9, 2015  /  No Comments

recby Angela Guess

Renee Caruthers recently wrote in Fierce Finance IT, “The finance industry has more than its share of data-intensive processes. But at least one technology firm is making the case that the answer to these challenges is not the traditional approach of ramping up data processing power – it’s smarter data processing. With a semantic technology-based platform, Recognos Financial is taking a different approach to data processing and applying it to financial processes as wide-ranging as securities master management, onboarding and KYC compliance.”

Caruthers goes on, “In mutual funds, for example, the company has assembled a securities master file for the mutual fund industry using a variety of tools from semantic technology to machine learning. As an Edgar distributor, Recognos has all mutual fund prospectuses and amendments, and it uses a combination of technologies to extract data from those unstructured data sources.’Our platform uses about six different methods of data extraction. At the top of the list and the most complex is semantics, but we also use NLP, regular expression, machine learning and machine learning with human intervention,’ said Drew Warren, Recognos CEO.”

Caruthers continues, “Warren estimates there are approximately 250 data points the company pulls from prospectuses and amendments to create its mutual fund securities master files. Semantic data aids the process because of three main benefits: its capabilities to structure unstructured data, integrate data very quickly, and deeply mine data for relationships. That last aspect is aided by the way semantic technology stores data – in triples, meaning data is stored in context, making it easier to align with other data points of similar contexts.”

Read more here.

photo credit: Recognos

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