How Language Processing Shapes Modern FinTech
FinTech and language processing algorithms have reached advanced stages of development in their own right. Together, they could potentially change the face of business and trade worldwide.
As FinTech reaches the hands of billions, companies are considering how they'll bridge communication gaps and process a massive influx of data streams. There's also a rising need to maintain regulatory compliance with dozens of countries at a time, and language processing tools can excel in parsing large amounts of complex legal terminology.
Finally, there's the promise of automating market research to pursue better investment strategies. For any combination of these reasons, finance companies are looking at any automated edge they can access, especially language processing tools.
Already Within Arm's Reach
By now, virtually everyone has used language processing or experienced its effects through a financial institution or one of its digital properties. Banks and other institutions are increasingly leveraging natural language processing (NLP) to streamline important routine tasks, including:
Automating report research and analysis
Assessing risks and predicting the investment values
Finding key information in large volumes of data
Parsing basic customer service requests
Improving the user experience (UX)
Not surprisingly, language processing in its current state is best suited for routine, or even rote, FinTech tasks.
That's certainly the case for "Erica," Bank of America's NLP assistant. It's already helped millions of people perform essential basic functions without a live teller at any time of day. Based on a large language model (much like GPT), the app provides notifications and chatbot functions.
Such large language tools have proven highly effective for various right-or-wrong, numbers-based tasks (such as verifying or moving balances). But that's just where it is today. Surprisingly, the more promising automation functions in FinTech involve large volumes of text. According to the MIT Sloan School of Management, the unstructured nature of text makes it harder to use. It's an ironic twist—resolving the complexities of language is precisely what makes NLP useful.
However, it's not yet sufficient to, for example, parse earnings reports in real time. That's where the real gains may be found for investors and key decision-makers at investment firms. Once FinTech language processing tools achieve high-speed text parsing, the ROI of language processing will go from helping institutions merely cut costs to actively securing new revenue streams.
A New Technological Arms Race
NLP is getting faster, but is it fast enough to offset several other high-tech financial trends? For instance, even advanced text analysis can take several hours to identify critical information amidst a sea of ongoing financial reports. The problem is that such information becomes obsolete faster and faster daily.
A few hours seems quick by yesterday's standards. But transaction speeds and other essential FinTech metrics are continually speeding up. It leaves language processing models struggling to close a gap that other FinTech advancements keep widening. Ultimately, these innovations become available to everyone who wants them. Who will hold the advantage then?
Overall, there are significant challenges to overcome before determining what role language processing will play in the investing world. What's most interesting, though, is how these uncertainties have still given way to greater adoption of language processing, not less.
Faster Adoption—Uncertainties and All
Financial institutions are quickly adopting NLP, even as they grapple with what to do with it. That's not to say that the current generation of language tools is limited to supplementing other, more important functions. It's simply unclear which finance-specific functions will have the best returns.
Companies are motivated by the urge to keep up and take advantage of any incremental benefits language processing will provide as it improves. As discussed, menial and replicable tasks have been the tip of the spear for language processing in FinTech—just as it's been in the language field more broadly.
By freeing up enormous departmental resources (such as with chatbots), language processing is already generating higher ROI for businesses generally. Improving financial performance validates these tools. What's still to be determined is the level of complexity that language processing can handle.
For now, NLP tools have made shorter work of routine processes. They allow financial companies to focus more attention on resolving more complex challenges. Both the market and finance sectors are as complex as ever, driving the need for better solutions. At the same time, those solutions are exposing all companies to greater competition and uncertainty.
FinTech is unique in that it can reap the same advantages of language processing as businesses in general, plus whatever untapped utility it will have for finance specifically. At its most fundamental level, this includes identifying optimal investments and business decisions. Other promising avenues include:
Automated data capture and transcription
Data enrichment for more identifiable and searchable text
Mapping financial relationships
Updating financial data across the web, app, and other communications
What about the big picture, though? The most competitive FinTech companies are interested in how they can use superior language processing capabilities to find a more lucrative market position.
Sentiment Analysis and Predictive Analytics
Separately, sentiment analysis and predictive analytics have revealed impressive insights into decision-making and brand-consumer relationships. When used together, they provide an ongoing glimpse into the behavioral science driving market dynamics. Their combined use has already proven its merit for markets at a smaller scale (e.g., a single brand and its loyalists), and this is just the beginning.
It's logical to assume the value of sentiment and predictive analytics will increase proportionately to the power of language processing tools. The effect will be better financial choices at scales ranging from product development to major investment moves.
Consistently predicting a market's behavior with empirical data has always been a dream for investors. It's too early to expect such lofty achievements any time soon. But it's possible that the most sophisticated applications of FinTech NLP could be the crude beginnings of a path in that direction.
Improved FinTech Security
Fraud detection is another crucial area where language processing can excel—aided, of course, by human review (the current norm in most automated data-processing services). Without automated tools, most banks are left detecting fraud after the fact, which is far from ideal if outright prevention is possible.
Real-time analysis will be a game changer for fraud detection, especially as blockchain-backed proofs of authenticity and authorship remove much of the uncertainty surrounding wholly digital properties. How long blockchain encryption and real-time language analysis can withstand the next significant upheaval in computer science, though, is hardly set in stone.
The Main Role of Language Processing in FinTech Today
As the turn of the century taught us, it's essential to ground speculation in a keen understanding of present capabilities. Not surprisingly, the most stable role of language processing in the financial sector is the same as for the internet generally: localization.
Before any FinTech tool can function as intended, it must bridge the same language and cultural barriers as any other digital property. While language is more of a medium than a property, its beauty is that it can function both ways, much like a financial currency. Above all, language processing in FinTech is about arriving at the most mathematically precise next-best move, even as the world seems to defy prediction.
Perhaps what's most intriguing is how the financial world seems to hinge more on language than on numbers. Strictly speaking, only time can tell how much further language processing models can penetrate the mechanics of global finance.