Natural Language Processing (NLP) belongs in finance because it unlocks the value hidden in all those text-heavy sources that numbers alone can’t capture. Think about it: important information lives in earnings call transcripts, regulatory filings, or news articles about financial markets.
Finance used to be all about numbers: spreadsheets, budgets, forecasts. But today, there’s a new expectation: explain what the numbers mean. That means understanding CEO statements, earnings call transcripts, market commentary, and Environmental, Social, and Governance (ESG) disclosures.
That’s where NLP in finance comes in. NLP helps finance teams analyze, interpret, and summarize large volumes of written material. It’s how you can understand what’s buried in an extensive Management Discussion & Analysis (MD&A) section without reading it line by line.
NLP gives you the tools to go from raw data to rich stories, ones that executives and board members need to hear.
Your executives don’t want to wait for manually crafted reports. They expect fast answers and explanations, not just spreadsheets. That requires pulling information from earnings call transcripts, regulatory filings, and internal commentary.
This is all possible with the help of NLP tools. They process text at scale, summarize key themes, and even suggest talking points.
For Financial Planning and Analysis (FP&A) teams, it’s a game-changer: instead of spending hours reading reports, you can spend minutes understanding them.
Here, we’ll discuss the many uses of NLP specifically in finance, FP&A tools that utilize it, and what the future holds for natural language processing.
The DNA of Financial Textual Data
Textual data surrounds finance teams.
Naturally, this data is text-heavy, unstructured, and often ignored in formal analysis.
The problem isn’t just variety, it’s scale. A single quarter might generate dozens of pages of narrative content. Multiply that across subsidiaries, regions, and product lines, and you have thousands of pages of unstructured data every year.
No one has time to read it all, but it contains signals that explain or support your numbers. That’s where Natural language processing excels: it breaks down vast volumes of text, finds what matters, and presents it in ways humans can use.
7 Core NLP Techniques Every Finance Pro Should Know
You don’t need to code to benefit from NLP, but it helps to understand the basics. Knowing what these tools are doing behind the scenes can make you more confident in using them and more effective when reviewing their outputs.
Here are seven techniques that power most modern NLP applications in finance:
1) Named Entity Recognition (NER)
NER finds names of people, companies, currencies, and figures in text. It’s useful for quickly extracting contract values, supplier names, or dates from documents.
NER also helps identify relationships between entities, such as who’s buying from whom or which teams are involved in a transaction, without reading the full document.
2) Sentiment Analysis
This is all about reading tone. Is the text positive, negative, or neutral? It’s often used in earnings call analysis, social media monitoring, and customer feedback reviews.
In finance, sentiment analysis can be applied to analyst reports or CEO statements to spot confidence dips, risk warnings, or growth optimism.
3) Text Summarization
You’ll likely find that summarization is especially valuable for time-sensitive reviews. Just imagine being able to scan a 60-page ESG filing or a dense MD&A section in seconds.
It condenses long documents, like annual reports or policy updates, into short, digestible summaries. It can also be tailored to highlight specific categories like “financial outlook” or “risk factors.”
4) Topic Classification
Topic classification automatically tags content by theme: performance, compliance, sustainability, risk, etc. This organizes and prioritizes information without your manual effort.
This is useful for routing incoming communications to the right team or filtering through large collections of contracts or vendor documentation by topic.
5) Question Answering and Natural Queries
When you want clear, contextual answers based on your reports, the question answering and natural queries powers of NLP provide them.
These systems combine natural language understanding with direct data access, so you don’t need to write formulas or filters. They’re becoming popular in FP&A tools that offer AI chat interfaces.
6) Entity Linking and Relationship Mapping
Beyond merely identifying terms like NER, entity linking, and relationship mapping, it also connects them to recognized records or databases.
For example, linking “Apple” in a contract to your internal Apple Inc. vendor record. This helps automate reconciliation, audit trails, and compliance checks.
7) Language Translation & Localization
For multinational teams, NLP can translate financial documents while preserving accuracy in key terms and numbers. For example, it can make the content usable across global finance operations, especially when reviewing international filings or contracts.
Each of these tools makes it easier to read less and learn more. When combined, they allow finance professionals to extract insights that used to require hours of manual reading.
All in all, NLP is a practical upgrade to how you work with words in finance.
High-Value AI-Based FP&A Tools
NLP is already reshaping how finance teams work. The most useful tools combine language understanding with automation, helping you move from manual reporting to on-demand insight.
Here are a few ways these AI FP&A tools are being used today:
Narrative generation
FP&A tools like Datarails can automatically generate financial commentary based on your numbers. Instead of writing from scratch, you get a first draft that highlights key changes, trends, or risks. This is especially helpful when preparing board decks, variance explanations, or monthly close summaries.
Conversational reporting
Certain platforms also let you ask questions in natural language—like “How did Q2 revenue compare to forecast?”—and get instant answers. These tools remove the need for manual slicing and filtering, which saves time and increases accessibility for non-technical users.
Variance explanation
Some tools analyze actuals vs. forecast using historical commentary, system logs, and trends. NLP helps explain the “why” behind changes in performance. They surface contextual information, such as a spike in travel spend linked to a sales initiative, so you’re not digging through notes or emails to find it.
Sentiment monitoring on external sources
NLP also extends beyond internal data. Some tools use sentiment analysis to monitor news, earnings calls, and filings for shifts in tone or reputational risk. Investor relations, legal, and risk teams use these features to stay ahead of emerging issues.
Each of these tools applies natural language processing in a focused way, making common financial tasks easier, faster, and more informative.
Whether you’re writing reports, answering executive questions, or tracking market tone, they give you a smarter way to handle the growing volume of financial data.
Building the NLP-Ready Finance Platform
Fortunately, you don’t need to start from scratch to use NLP.
Here’s how to get your team ready:
- Centralize your data: Gather all financial text, from reports to emails, in a digital, searchable format. Avoid PDFs when possible. Use cloud storage or a knowledge base to keep it organized.
- Choose finance-aware tools: Generic NLP tools exist, but many don’t understand finance language. Select platforms purpose-built for the finance industry or those that let you train models on your documents.
- Start with a specific problem: Maybe you want to summarize earnings calls, monitor ESG language, or reduce manual reporting. Pick one pain point and run a pilot.
- Involve analysts and decision-makers: Train your team on how to use the tools and interpret output. Finance teams don’t need to build models, but they do need to trust the insights.
- Monitor security and compliance: Especially when using third-party AI tools, ensure that proper data protection protocols are in place. Many financial institutions use on-premise deployments or private clouds for sensitive information.
The goal isn’t to automate everything. Instead, you can support better decisions with less effort.
Future Outlook: LLM-Native Financial Workflows
Large Language Models (LLMs) are making NLP even more powerful.
Here’s what most experts assert that the future holds:
Agentic forecasting assistants
Soon, AI tools won’t just respond to questions, they’ll take action.
Picture an AI that reads this quarter’s earnings, compares them to market news, adjusts your forecasts, and highlights red flags before your morning coffee.
This kind of autonomous assistant can reduce turnaround time for forecast updates and scenario planning.
Voice-driven ad-hoc report creation
You’ll be able to say things like, “Give me a Q4 comparison by product line,” and receive a full report in a few minutes, complete with text commentary.
This allows for faster insights during meetings, board prep, or cross-functional collaboration.
Continuous ESG and sustainability monitoring
ESG regulations continue to expand, so finance will need to track more qualitative disclosures.
NLP tools will automatically read ESG reports, flag emerging risks, and benchmark your company’s sustainability language against peers. In return, you can expect help with compliance, brand management, and long-term investment strategy.
Conclusion: Where Do You Go From Here with NLP in Finance?
The good news is that you don’t need a data science team to start using NLP.
Start by asking:
- Where are we reading too much and learning too little?
- Where could we answer faster if we had help parsing the words?
Pick that point. Run a pilot. Measure the results.
Then scale.
Datarails offers AI-driven FP&A tools that already incorporate NLP features like automated storyboards and visuals, plain-language querying, and real-time insight delivery.
Want to see NLP in action for your team?
Book a demo with Datarails today.
