AI: Natural Language Processing and the Battle for Unstructured Data

AI: Natural Language Processing and the Battle for Unstructured Data

Increasingly banks are turning to the field of natural language processing (NLP) and machine learning to extract valuable information from voice, documents, and audio to boost productivity on trading desks. It’s all part of a broader push to gain efficiencies by training machines and bots to analyze language, capture insights, and replace manual tasks and drive workflows further downstream.



With digital transformation in full swing, trading desks are inundated with emails, voice calls and chat to process and analyze. Most of this data needs to be captured, tagged and stored for regulatory purposes.

But in capital markets, keeping up with the torrent of research reports in email, quote requests, and chat conversations with clients can be impossible to handle manually.

“Trading firms are overwhelmed with unstructured data as they have many forms of communication, such as the phone, emails and chat,” said Richard Johnson, VP market structure and fintech at Greenwich Associates, moderating the July 25 webinar “Artificial Intelligence on the Trading Desk.  “Then we have the competitive dynamics on the trading desk, which means there is falling head count and fewer traders handling the work.”

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FlexTrade is a global leader in broker-neutral, execution and order management trading systems for equities, FX, options, futures and fixed income. A pioneer in the field, FlexTrade introduced the first trading system that allowed clients to control and customize their proprietary algorithms while maintaining the confidentiality of their trading strategies. Change is the only constant in electronic trading. That's why FlexTrade is continuously upgrading its products and services. All can be tailored to meet the demanding requirements of a global client base of more than a 225 buy- and sell-side firms.
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