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Natural Language Processing, the Shift from Surveilling Traders to Supporting Them

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By Oliver Rooney, VoxSmart.

Natural Language Processing (NLP), although not a new concept, is increasingly being discussed within Financial Markets. Despite its tenure in everyday services such as chat boxes, Alexa and Hey Google amongst others, the way in which it is being utilised in financial markets is changing for the better with firms set to reap valuable rewards from its early adoption.

In this blog we take a look at how different sectors within capital markets are utilising NLP technology having established in our previous blog – What is Natural Language Processing and how does it work?

Why is NLP needed in capital markets? 

Trading floors are a noisy environment, even on today’s evermore technologically advanced trading desks. Traders and sales teams have to manually decipher information through many different communication channels in both text and audio formats however, with the vast quantity of data in circulation and the volatility of this information, opportunities are being missed.

Furthermore, financial and trading conversations are awash with industry specific jargon. Traditional speech recognition devices without in-depth and thorough training have a difficult time in making sense of such language and often fails to adequately interpret the true meaning of such speech. This can be seen cause further delays with manual input and review necessary, with traders missing crucial insights from their data.

Here are just some of the examples of where NLP is used in the financial sector:

Big Banks – Deal Opportunity Detection

Big banks in an effort to leverage the data collected need to be engaging an NLP platform as to detect deal opportunities, and flag missed opportunities, through speech analytics. Furthermore, NLP enables the front office to identify profit and loss spikes increases revenue opportunities and other insights into how traders are selling with reports gleamed from communication data.

As CEO and ex-fixed income trader, Oliver Blower in an interview with RegTech 100 stated; “On an average trading floor and an average day, a trader or salesperson is missing more opportunities than they capture”. In order to combat this issue an intuitive NLP platform is needed to bring order to the chaos.

By utilising NLP to better understand the nature and quality of trading relationships, not only will this help analysts to understand the pricing efficiency of market makers but also assess which relationships add the most value to a market. This serves to reduce operational costs as well as optimise and focus sales relationships at different levels.

Trading desks – A source of Indicative Pricing

With the volatility and speed of such markets, failing to capture real-time prices automatically puts dealers on the back foot when it comes to securing the best price for customers.

In tackling this issue NLP serves to automate pricing, by feeding pricing engines with real-time pricing information taken from live communications. Through this indicative pricing feature, traders are ensuring the best prices for clients whilst boosting revenue through increased speed of trade.

What’s more, NLP enhances overall trading floor efficiency by streamlining the order entry process for traders. Currently this is a manual task and falls to the responsibility of each trader to input within a limited timeframe under regulation (MiFID II).  In this was do we see NLP to provide traders with additional trading time, subsequently optimizing profits.

Asset Managers – Sentiment and Intent Analysis

Many Buy Side firms are increasingly coming up against a vast amount of research to be undertaken in order to glean insights from analysts’ documents regarding earnings estimates. While it may take some time for analysts to update numerical forecasts, parsing text reports using NLP to reveal sentiments and intent enables managers to capture a picture of firms’ overall position in the absence of standard numerical estimates.

Additionally in terms of due diligence, NLP provides firms with the opportunity to streamline client screening processes by scanning a profile to identify whether a client meets the pre-defined criteria.

NLP is diverse and adaptable to specific industry needs, tailored to solve industry needs from universal to niche. The benefits to be ascertained make its adoption a no brainer with incalculable possibilities.

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