7 Use-Cases for Natural Language Generation in Finance

Two peopling making a financial report

Peering into an ocean data that’s complex and interwoven can be intimidating, especially when it’s up to you to grasp the underlying meaning and report on it. Through the use of natural language generation in finance, you’ll be able to convert large and disperse financial datasets into meaningful written narratives with a press of a button.

What Is Natural Language Generation in Finance?

You might have heard about natural language processing (NLP), which is an AI technology that converts textual, written data into numerical data representing predictions and classifications. Conversely, natural language generation (NLG) converts all types of financial data into human-understandable content.

The vast amount of data produced in finance is far greater than any single person or organization can properly consume and extract meaning from. There’s just too much data and it’s everywhere. NLG helps by digging up some of the underlying meaning and produce written narratives that describe it in understandable terms.

Not only is the amount of financial data huge, but it can often be unstructured, decentralized, and ill-formatted. NLG can learn how to interact with this type of data as it becomes better acquainted with it, which is why NLG is an excellent technology for the financial services industry.

NLG also allows employees – from executives to analysts – to devote their time to more value-added tasks instead of repetitive, procedural ones. Operational procedures at financial firms can take a lot of time, especially due to increased regulations, so being able to free up valuable time can have a direct effect on a firm’s bottom line.

How Natural Language Generation Can Help Your Firm

Generated written narratives can be used in several different use-cases. Whether it’s used to assist CIOs, analysts, compliance officers, portfolio managers, or even the public, anyone receiving information from a well-trained NLG model can have a competitive edge.

1. Financial Reports

It’s not uncommon for a team working in finance to spends hours or days producing monthly reports. The amount of data needed to be scrounged up is massive and it’s likely it needs to be cleaned up and fed into some other software to extract a sliver of meaning. This generates a lot of wasted working hours that could’ve been spent on more value-added tasks.

It just so happens that producing periodic financial reports is a perfect use-case for NLG. With a well-trained NLG model, you can cut your reporting time down from hours or days to seconds. One of the great qualities of NLG is that it’s able to produce meaningful written interpretations into a first draft. With a first draft, your team can easily massage the report to match your companies persona and branding.

2. Regulatory Filings

After a financial crisis comes a rush of new regulations that can create a huge burden on financial services firms; big and small. The costs can come from several sources, including time spent generating reports that need to be filed, being fined for forgetting important information in a filing, or even being fined for missing a deadline.

Through the use of NLG, you can reduce the burdens and costs of regulatory filing tremendously. Apart from NLG being able to produce reports, it can also generate insightful notes for very specific filings that require nuanced understanding. For example, an NLG agent could help in the filing of a 10K report by producing written content in a specific narrative (positive, neutral, or negative) that’s fitting for a specific organization.

3. Executive Summaries

Everyone’s time is valuable and it should be used to produce value instead of being bogged down by tedious, repetitive tasks. However, everyone must be on the same page following the conclusion of an important event that happened within your organization, which is why executive summaries are so important.

Summarizing a long report, proposal, or group-related project is usually the job of one or several people and can often require the collection of disorganized data points. With NLG, quick and easy executive summaries can be generated to help teams deliver concise, diluted information quickly. However, more advanced NLG models are needed for these sorts of tasks since the data sources can be both disorganized and unstructured.

4. Suspicious Activity Reports

One of the most important actions a financial services company can take is to prevent financial fraud and money laundering activities from occurring at their place of business. With financial criminals using more advanced technologies toward more sophisticated attacks, banks and financial services firms should also be using more advanced technologies to combat this behavior.

Artificial intelligence can be used to effectively combat financial crime by empowering compliance teams to better understand the large and complex data they need to report on. NLG can help distill important information extracted from global financial transactions made through many types of intermediaries. There’s no question the use of NLG can have a huge impact on the anti-money laundering and financial fraud reduction efforts of a financial firm’s compliance team.

5. Strategic Advice

There will come a time when AI is so smart that it’ll be at our detriment if we don’t listen to them. As creepy as that may sound, it’s simply the direction AI is moving toward. While AI is mostly used toward very specific tasks, it’s becoming more capable of discerning general themes and subsequent actions to take.

In finance, any relevant information can be used to produce a competitive edge. With NLG, it’s possible to generate strategic advice on specific actions by analyzing data in a certain domain. Like most AI today, NLG is better at generating good results when it’s able to focus on a narrow domain, but over time it’ll be able to provide broad strategic advice based on data collected on a global scale.

6. Breaking News

Regardless of where you get your news, most people only look at headlines and potentially the first paragraph or two. In finance, everyone’s trying to get a macro understanding of the world’s events. However, there are so many events to report on that it makes little sense to dedicate employees’ time to generate this breaking content.

While long-form content does require a human touch, very short content, like breaking news, can be generated via an NLG agent without much oversight. One of the best aspects of NLG is that it’s very good at contextualizing lots of disparate data into clear, written content. This is especially true for breaking news, like unpredictable quarterly earnings reports, central bank rate changes, or crop reports.

7. Chart Annotations

You might have seen how many monitors someone’s desk has who actively participates in the financial markets. The sheer amount of data used to produce all the charts needed by traders, portfolio managers, and analysts is staggering. What’s worse is that it’s up to them to be able to derive valuable information from these charts.

While quantitative models are very good at sifting through noise to find a signal, they’re still producing data that needs to be interpreted. NLG can be effectively used – by itself or coupled with predictive models – to annotate specific locations of charts where anomalies might show up that foresee fortune or risk. With the help of NLG, active market participants can have a better macro-level viewpoint to allow better decision making.

Try out Natural Language Generation

Whether your an executive, a vice president, or an analyst, using NLG can be a big win for your firm if used correctly. While it certainly requires a technical understanding to properly implement, the effort will be well worth it in the end. Regardless of the type of work you do on a daily basis, there’s most likely a use-case for NLG that’ll amplify the value you add at your organization.