How capital markets are leveraging Natural Language Processing technology.

As demand for NLP soars, discover why, and in what way capital markets are adopting this cutting-edge technology to capitalise on trade communications.

Everyday life exposes us to vast quantities of data from different sources; via email, phone, the internet or social media.

In a similar vein, businesses too are subject to constant data streams from internal and external communications or other feeds. While the human brain is extremely efficient in processing this data, filtering out the important from the unimportant information almost instantaneously, businesses face the daunting challenge of scaling data management and analysis.

With multiple sources across numerous departments often information can be lost amongst the noise with no central filtering system.

Although many electronic systems can ingest structured data, these systems fail to capture unstructured data such as missed opportunities and information mismatches. Within financial markets, the processing of such information is paramount to the success of a firm. In order to remain competitive and profitable in fast-moving and volatile markets it is essential that movements or positions aren’t missed by traders or sales departments. Remote and hybrid workplaces have complicated matters and there has surfaced a growing need for more effective tools to monitor trade and communication data, whether on or off the trading floor.  

So how can firms bring order to this unstructured data, converting it to usable insights? The answer lies in Natural Language Processing (NLP), which helps financial institutions to process, analyse and index information from a range of sources including audio and text.  

Demand for NLP is extremely high. According to global business data platform Statista, with an estimated year-on-year growth of 36.5% for 2022 alone, it is clear that businesses are maturing to the benefits to be derived from the technology. Furthermore, the NLP market is predicted to be almost 14 times larger in 2025 than 2017, increasing from around three billion U.S. dollars in 2017 to over 43 billion by 2025. 

Continue reading below to find out more about NLP and how it’s changing the game when it comes to data analysis.


Put simply, Natural Language Processing (NLP) is a technology used to help computers understand human language. The technology is a branch of Artificial Intelligence (AI) which focuses on making sense of unstructured data such as audio files or electronic communications.  

NLP combines both computational linguistics with machine learning (ML), statistical analysis and deep learning models to break complex language and conversations down into machine readable data, detailed reports and actionable insights.  

Intent and sentiment are extracted through the context and structure of each conversation, enabling the technology to understand communication, both written and spoken, in the same manner humans can.  

What is deep learning?

Deep learning is a subcategory of machine learning, concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. This process mimics the way the human brain operates and processes data.

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