Artificial Intelligence Guest Posts

4 Fintech Applications of Natural Language Processing

4 Fintech Applications of Natural Language Processing
Financial institutions are turning to Natural Language Processing (NLP) to help them cut through the noise in the fintech industry.

For most industries, keeping up with the pace of technology and information is a challenge. The financial services industry is no different, and traditionally, it’s been one to embrace change more slowly—and for good reason. Highly regulated industries like finance can’t afford to compromise privacy, waste time, or make themselves vulnerable to the hurdles that come with integrating new technology into already-existing systems. But what if said technology had the power to streamline tedious processes, sift through massive amounts of data quickly and accurately, thus giving your employees and business a competitive edge?

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Whether the promise of these advantages is enough to pique your interest or you’re in the ‘if it isn’t broken, don’t fix it’ camp, the fact of the matter is, financial institutions are getting buried in data. Humans simply can’t keep up, and as a result, adopting technology to augment once manual tasks is a business need, rather than a ‘nice to have.’ This is why smart financial institutions are turning to Natural Language Processing (NLP) to help them cut through the noise and gain important industry insights. NLP not only has the ability to decipher the huge amounts of data firing from all cylinders, but actually understands its contents and how to present the information in a clear, easily digestible way that helps financial professionals do their jobs.

While NLP is making great strides in industries from healthcare to retail, here are four ways the Artificial Intelligence (AI)-powered technology is changing FinTech for the better.

  • Reading Algorithms

When we think of finance, the first thing that comes to mind is numbers. But one of the most impactful applications of NLP in finance has nothing to do with math, and everything to do with words—reading comprehension, to be exact. In fact, almost all financial news is now coming from algorithms, from Bloomberg Terminal to SEC filings, and even tweets. Years ago, you had to read all of this information yourself—but in this age, that’s not feasible. Time is of the essence in the fast-paced finance industry. Going from hours to minutes to distill important information makes a huge difference, and NLP is responsible for the quick analysis and delivery of this information to the decision-makers who need it.

  • Writing Algorithms

What may come as a surprise is that most financial content today is not just read, but written by NLP-powered algorithms, too.

For example, in past years, an analyst would read and write about S1 filings, but now, these are automated in the form of news writing, blogging, and even tweeting. The algorithm reads the article, decides what’s important to write about, and when and where to post it in order to get pertinent information out. Accuracy and timeliness are of the utmost importance here, and with NLP, you don’t have to question whether you’re sacrificing one for another.

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  • Understanding Jargon and Context

Another useful application of NLP is turning unstructured data into a more usable form. For example, not all data is found in text: sometimes it’s presented during an earnings call, presentation, or from a live news report. NLP can capture this information, connect it to other siloed data sources, and understand the context to provide more actionable insights.

Much like in the medical field, financial jargon presents greater challenges when it comes to searchability, especially if you consider different industry-specific terms used by different institutions. NLP can intelligently link these to paint the full picture of what’s going on, helping to deepen industry knowledge and win an edge over the competition.

  • Inferring Relationships with Knowledge Graphs

Relationships between things and text must be understood for NLP to provide true value. It’s even more effective if these connections can be extracted quickly and easily. Fortunately, advances in knowledge graphs have made this possible. Knowledge graphs represent a collection of interlinked descriptions of entities—objects, events, or concepts—and put data in context to provide a framework for data integration, unification, analytics and sharing. Take an acquisition, for example. You don’t just want to know what company got acquired, but also the date, the amount, if it’s public, and other details, all of which can be rendered from analyzing text with NLP.

Similarly, if you’re looking at financial news, executive movements, issuing stock, equity, and more, you care about more specific details that require NLP to understand the relationship between the two entities involved.

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With the ability to provide real-time and accurate insights to financial professionals, NLP technology is widely applied across the industry. Time is money and with data growing by the minute, without the right tools, companies lose their edge to more savvy opportunists. NLP provides a great and immediate opportunity for financial businesses to capitalize on that, remaining agile and making the best decisions possible with the best and most current information there is.

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