Why the finance industry is deploying natural language processing

November 4, 2020

As volumes of data continue to increase, natural language processing is emerging as an important element of financial analysis. Businesses are continuing to embrace machine learning to enable quicker and more efficiently informed decisions. Financial analytical businesses are shifting their attention towards natural language processing to analyse data significantly faster and more accurately than possible by humans. 

Many assume that financial data is largely numerical rather than textual but industry experts suggest data that enables timely decisions generally comes in text. Text is unstructured data and is typically more complicated to use, which is where natural language processing can play an important role. A form of machine learning, NLP can parse complex elements of audio corresponding to business and finance, including phrases, numbers, currencies and product names.

For example, earnings reports are one method that is released as a text. Extensive time is required to transform this information into structured data. NLP can generate transcriptions in just a matter of minutes, providing analysts with a competitive advantage.

NLP may be relatively new to the finance world, but as it continues to accelerate, the industry can utilise the years of research and development from other technology leaders, including Google and Facebook. These types of businesses have worked with NLP for several years and can provide a clear path for the finance industry.

Whether your business is researching a company or exploring data sets on a particular region that is beyond capable of a human doing, businesses will rely on these types of technology even more. There are several ways in which NLP can improve decision making and enhance the response time of financial businesses:

Automation: NLP can replace certain manual tasks and convert unstructured data into a more effective and usable form. For example, this can include, management presentations or acquisition announcements.

Enhancing Data: Once unstructured data is collected, applying context can make the information both searchable and actionable. Machine Learning can enrich raw information, identify particular sections that may have a financial impact or other particular areas of concern to the business.

Improve Search and Discovery: The finance industry is actively seeking to find a competitive advantage in terms of data variation. However, what is important is delivering a search experience that is as efficient and effective as the google search bar that customers are used to. It can be very challenging searching data at a bank or hedge fund. Financial analysts emphasise the need for intelligent systems capable of understanding the industry.

For financial businesses looking to gain the benefits from these technology services, the barriers to enter the market are significantly lower than in previous years, due to technology being more affordable and easier to implement. With the advancements in technology today, it’s realistic to implement innovative NLP in finance without having the skills or experience in machine learning.

The competition between major technology businesses has enhanced the machine learning environment for interested stakeholders. Technology leaders are investing significant money into creating efficient machine language systems and in pursuing market dominance, have generated available frameworks for other businesses. 

Businesses are still trying to determine the most effective way of implementing machine learning and for the finance industry, there isn’t necessarily a single solution. Companies could create machine learning products or build their data science team. Machine learning can be applied pretty much anywhere, from a low-level data collection point to a high-level client-facing service.

The recognition of having valuable data that isn’t being fully utilised is the general drive for implementing machine learning. Businesses understand they have all this data that is too much for humans to use, so how can machine learning and natural language processing be applied? For financial businesses, which often are cautious with adding new technologies like machine learning, this understanding is critical. As more people see the products and understand the processes, they begin to realise how it works and values it offers. 

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