How NLP helps fintech pros get an edge
Historically, the financial services industry has been one that approaches technology with caution.
Feasibility, regulation and privacy have all been barriers to tech adoption over the years. But that’s changing—a move brought on not only by choice but necessity. Financial institutions are drowning in text data, from compliance reports and contracts to news stories and even social media musings. The pace of information has accelerated exponentially over the past decade, and traditional processes just can’t keep up.
As a result, organizations are turning to automation solutions to help them cut through the noise and gain important insights more quickly and accurately than humans are capable of. This is why fintech is turning to Natural Language Processing (NLP): a subset of AI that has the power to help make fast, better-informed decisions, giving users a competitive edge. And with the ability to parse textual data, while understanding the nuances of industry jargon, numbers, different currencies, and company names and products, it might just be one of the most significant technologies to hit the industry.
What might come as a surprise is one of the most impactful applications of NLP in finance has nothing to do with crunching numbers, but rather, reading and writing. In fact, almost all financial news is now coming from algorithms, from Bloomberg Terminal to SEC filings, and even tweets. In the eighties, you had to read all of this information yourself—in 2021 that’s simply not possible. To achieve this would be akin to trying to watch every video on YouTube. Perhaps with unlimited hours at your disposal this would be doable, but timing is of the essence. Taking twenty minutes vs. two-minutes to distill important information makes a huge difference, and NLP is responsible for the quick analysis and delivery of this information to the professionals who need it fast.
What’s less known is that a lot of this content is actually written by 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 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. Again, accuracy and time are vital factors here, and with NLP, you don’t have to question whether you’re sacrificing one for the other.
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. As mentioned above, financial jargon is another challenge when it comes to searchability, especially if you consider different 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 market.
With advances in NLP-powered knowledge graphs, relationships between things and text can be extracted quickly and easily. Take an acquisition, for example—you want to know what company got acquired, the date, the amount, if it's public, and other details—all of that can be rendered from analyzing text with NLP. Similarly, if you’re looking at financial news, executive movements, issuing stock, equity, etc., you care not just about the company name, but who acquired who, and other pertinent details that require NLP to understand the relationship between the two entities.
The adage “time is money” drives many financial services use cases, so the ability of NLP technology to provide real-time and accurate insights opens many opportunities. With a lot more data than even a few years ago, coming at speeds that even a massive team of human experts cannot process, automation tools are no longer just a cool feature but a necessity. Fortunately, all signs point to things heading in the right direction. According to recent research from , the influence of tech-savvy consumers, the looming threat of big tech companies, and the shifting attitudes of regulators toward new tech are all impacting the financial services industry. It will be exciting to watch how technology adoption takes off over the next year.
NFTs: A token of trust in the digital world
NFTs have recently taken the world by storm, as media headlines in the last month will attest. The digital artist Beeple sold the NFT for one of his pieces for a record US$69mn in a Christie’s auction. Jack Dorsey just sold a digital version of his first tweet for over $2.9mn in the same way, with the buyer comparing it to the Mona Lisa. The band Kings of Leon are even selling their new album in the form of an NFT.
In simple terms, NFTs, or non-fungible tokens, provide verification of ownership of a digital asset. They are unique digital tokens stored on a blockchain ledger, which means that they cannot be changed or tampered with. Traditional artworks, such as paintings or sculptures, are valuable because they are one of a kind and cannot be replicated. Conversely, digital files can be easily – and endlessly – copied. However, by purchasing an NFT, the buyer can prove that they own the rights to the "original" digital asset.
There have been mixed responses to NFTs’ sudden popularity, with some seeing it as the emergence of a new asset class, while others cannot wrap their heads around the idea of paying such large sums of money for a digital asset that can be duplicated.
However, surely this is a natural evolution in today’s digital world? As with traditional art, digital art is only worth what someone is willing to pay for it. In theory, anyone could have an excellent replica made of a traditional artwork if they wanted to, but a large part of art’s value is derived from its originality. Serious art collectors don’t want a copy. Countless people around the world have Matisse prints on their walls, but it isn’t the same as owning the original painting. Why should it be so different for digital art?
Blockchain is enabling monetary value to be assigned to the “digital twin” of a physical asset and, by virtue of distributed ledger technology, creating a virtual environment in which the authenticity of a digital asset or “twin” is a separate value in its own right - due to the unique corresponding verification on a blockchain. Digital twins have not instantly taken off in the mainstream, as the risk of duplication has been a significant deterrent – however, NFTs are paving the way for a new era of trust in digital assets.
For our part, we see NFTs as yet another way that blockchain is creating opportunities and shaping the world in which we live. Blockchain’s ability to record data securely and immutably is an incredibly important technological advancement, and it is no surprise that it is being capitalised on in so many different ways.
This powerful technology has certainly come a long way since its origins as the foundation of cryptocurrency, and we are seeing new applications every day. We set up Finboot in the first place because we could see the value of introducing blockchain to enterprise supply and value chains, and we’re seeing the technology deployed in a number of ways by our clients, from invoice reconciliation to the verification of sustainability credentials, giving them a competitive edge as well as building trust.
Some might be skeptical about NFTs but they would be wrong to dismiss it as a passing fad. NFTs effectively solve the problem of authenticity and, because the tokens are stored on a decentralised database, the record is public, significantly reducing the possibility of theft or fraud or theft. NFTs are a game-changer, and this is just the start.