Advanced Ways Financial Firms Are Using Sentiment Analysis

What Separates Sentiment Analysis From Other Artificial Intelligence?

AI text analysis tools that can digest large amounts of content have been around for a bit, and while having AI trawl the internet to identify trends quickly can be easy, those types of tools are interpreting words strictly as pieces of data. This creates a snapshot of what’s being talked about online, but just sorting words is a major oversimplification of language.

Communication is a lot more than the words that are said, especially online. Understanding tone, context, previous usage patterns and other harder-to-quantify parts of language is where NLP tools stand out. The way these tools are trained to convert language into data is a far cry from simply grouping common words and phrases together. This advanced analysis allows financial firms to more accurately understand market behavior, how customers are feeling and how their brands are being perceived.

Sentiment analysis also allows bulk market research to go beyond the written word. Analyzing video, audio and images — and the way they relate to other content on the same page — in everything from social media posts to internal emails and customer voicemails gives financial services firms a fuller picture of how the market is feeling and is likely to behave.

How Sentiment Analysis Helps Financial Firms Make Better Decisions

Understanding that more qualitative data is a positive is one thing. Understanding how to turn that unstructured data into something that’s helpful is another matter. Practicing good data governance, complete with human oversight and regular re-education of sentiment analysis tools, ensures those tools are offering accurate direction and not sending your firm down the wrong path.

Once sentiment analysis tools are configured correctly, it’s time to put them to use in one or all of the following ways.

1. Extracting Market Signal From News Coverage and Social Media Conversations

People are always talking, especially when it comes to their money. Opinions on how to spend, make or save one’s money can be easily found in newspapers and magazines, on the TV news, on podcasts, late-night social media posts, random Reddit recommendations, and your uncle’s fishing-focused Facebook group.

Deciphering which are valuable and which are not is easier said than done (take meme stocks, for example), and keeping up with a volatile and fast-moving world can challenge the most seasoned analyst. Because NLP tools, like other AI tools, are constantly learning, no tool is better prepared to meet reality — online or in the physical world.

Like the humans they work with, NLP tools aren’t perfect, and while they may be smarter than humans in some cases, they’re not able to perfectly predict the future. Still, they have a far better chance of seeing more clearly through the chaos of the internet than a set of human eyes.

Click the banner below to keep reading stories from our new publication BizTech: Financial Services.

2. Forecasting Price Movements Using Alternative Data

Even before the rise of AI, alternative data was having a big impact on the financial services industry. Analysts who were able to get ahead of alternative data trends — such as monitoring corporate air travel — could gain an advantage over the competition, albeit one that was typically short-lived.

Putting a sentiment analysis tool on the search for novel types of alternative data and novel ways to interpret that data can be an even more effective differentiator. NLP tools that can extract data from massive documents or hourslong meetings in the blink of an eye give financial institutions a reasonable expectation of alpha and an easier time pivoting as quickly as the market does.

3. Optimizing Portfolio-Level Risk Management

Sentiment analysis isn’t just for the big-picture direction of financial firms. The large-scale insights a firm generates eventually gets disseminated to individual agents and, in turn, their clients. Explaining what sort of sentiment these tools have discovered and why a firm’s advice is reacting to that sentiment can keep customers happy (something that, meanwhile, can also be quantified using sentiment analysis).

Perhaps most important, however, is the way NLP tools can check employee behavior to ensure mistakes in compliance — whether intentional or not — aren’t being made. Sentiment analysis tools do this by monitoring customer correspondence via email, direct message, phone call or other methods. Catching a red flag before it turns into a full-blown budgetary or regulatory crisis can help firms avoid potential disaster.