Financial narrative processing is an emerging field that combines natural language processing (NLP) and machine learning (ML) techniques to extract, summarize, and analyze the textual narratives found in financial documents.
In other words, it means using AI to read and understand the “stories” behind the numbers. Financial reports aren’t just rows of figures, either. They also include written sections like management discussions, earnings call transcripts, and footnotes.
These narratives provide context and insight into a company’s performance and strategies. FNP helps you automatically make sense of those written explanations, picking up insights you might miss or find hard to quantify.
With the explosion of financial information, FNP has become increasingly important for both researchers and finance professionals. Originally developed in academia, it’s also catching on in business.
With FNP, we can quickly identify key points from a 100-page annual report or detect the sentiment in a CEO’s letter to shareholders.
There has been an ever-present gap between traditional number-crunching and the qualitative analysis of financial storytelling, but technology, including FNP, has bridged it.
FNP Summary: Key Takeaways
- Financial narrative processing (FNP) refers to using advanced NLP and AI tools to analyze the textual (narrative) parts of financial documents alongside the numbers.
- Why it matters: The narratives in financial reports (like management commentary, earnings calls, and annual report sections) often hold key insights about a company’s performance and future outlook that pure numbers might not reveal.
- Academic origins: FNP emerged from academic research, with dedicated workshops since 2018, focusing on tasks such as summarizing annual reports, extracting document structure, and detecting cause-and-effect statements in financial texts.
- Business applications: Companies and finance teams are beginning to use FNP techniques to save time and improve analysis. For example, it can automatically summarize lengthy reports, gauge the sentiment of earnings calls, or flag risks mentioned in filings.
- Big picture: FNP helps close the gap between quantitative data and qualitative insights, allowing you to fully understand financial information. Rather than focusing only on numbers or only on narratives, decision-makers can make more informed choices.
Why Do Financial Narratives Matter in Finance?
We often focus on financial numbers—revenue, profit, budgets, and variances. But the narrative that accompanies these numbers is just as important.
Financial narratives include things like:
- The management discussion in an annual report.
- A CEO’s letter to shareholders.
- The notes explaining accounting choices.
These sections provide color and context, helping explain why the numbers look the way they do.
For instance, a revenue number might show a 10% drop, but the narrative might reveal that it was due to a supply chain issue or a one-time event.
Understanding the story behind the figures helps you make sense of the data rather than just seeing isolated metrics.
However, reading and digesting all this narrative content can be challenging, especially when dealing with lengthy reports or multiple documents. This is where financial narrative processing comes in.
It automates the analysis of those written sections.
By doing so, FNPs can highlight important factors or red flags you shouldn’t overlook. Analyzing the language managers use can reveal how the business is truly performing and what the managers really think about the company’s prospects.
In other words, the words in these narratives can signal a company’s confidence level or concerns about the future in ways that numbers alone cannot.
How Did Financial Narrative Processing Start in Academic Research?
The idea of systematically analyzing financial narratives took off in academic research around the late 2010s. Researchers in accounting and finance have long understood that corporate narratives (like annual report text) can offer insight beyond what’s in the spreadsheets.
Traditionally, however, such analysis was done manually on a small scale. For example, reading a few annual letters and scoring them for tone.
In recent years, automation changed the game. Academic experts in natural language processing teamed up with finance scholars to scale up this analysis using algorithms.
In fact, an academic workshop series called the Financial Narrative Processing (FNP) workshop has been running annually since 2018. These workshops bring together researchers to share methods for teaching computers to handle financial text.
There’s a growing interest in using automated NLP tools to extract insights from financial narratives, and small manual studies have been scaled up dramatically with machine learning and even deep learning methods.
For example, researchers have built algorithms to automatically summarize long financial documents, extract their structure (like identifying sections and a table of contents), detect cause-and-effect language, and even analyze the sentiment or tone of the text.
FNP research has gained a lot of traction, highlighting the growing significance of this field. Academic efforts prove that financial narratives can be analyzed systematically and usefully, laying the groundwork for practical tools businesses are starting to use today.
How Can Financial Narrative Processing Be Used by Finance Teams and Businesses?
While academics were early to the party, businesses are now picking up on financial narrative processing to solve real-world problems. The ability to quickly digest and interpret large volumes of text is a game-changer for finance teams strapped for time.
Here are a few ways FNP techniques are being applied in the business context:
1. Earnings call analysis
Every quarter, public companies produce transcripts of earnings calls full of management commentary and Q&A. NLP algorithms can analyze these transcripts for sentiment and even clues of deception.
For example, NLP-driven sentiment analysis can uncover subtle signs of executive confidence (or lack thereof) about a company’s future. This type of insight gives investors and executives a heads-up on market perception that goes beyond the raw financial results.
2. Automated report summarization
Finance teams can use FNP to summarize lengthy reports from competitors or regulators. You could feed a 100-page annual report into an FNP tool and get a one-page summary of the key points. This saves time and makes sure no important detail is missed.
3. Risk and compliance checks
FNP can also help identify risks and support compliance.
For example, a bank might scan loan notes for terms that indicate fraud or credit issues, and a compliance team could check that forward-looking statements in reports are properly worded. The technology can continuously monitor text for red flags like those that humans often miss.
In practice, many of these applications are becoming feasible thanks to advances in AI. Large language models—the kind of AI behind modern chatbots— have made it much easier to implement FNP.
Today, you don’t always need to build an algorithm from scratch or have a PhD in computer science to leverage these tools.
Many analytics and business intelligence platforms now offer features for narrative analysis (for instance, auto-generated report summaries or sentiment analysis dashboards).
As these tools become more user-friendly, finance professionals can take advantage of FNP without deep technical expertise.
Important Note
It’s worth noting that FNP tools are meant to support human analysis, not replace it. Your team’s expertise and judgment are still paramount. The technology just helps to make sure you’re not flying blind on the qualitative information.
Ultimately, an FNP can handle the heavy reading and highlight crucial points, so you can focus on making decisions with the full context in mind.
FAQs: Financial Narrative Processing
Is financial narrative processing the same as sentiment analysis?
Not exactly. Sentiment analysis (measuring whether text is positive or negative) is one component of financial narrative processing, but FNP is broader.
FNP includes sentiment analysis and other tasks like summarization, topic extraction, and detecting causal statements. Think of sentiment analysis as one tool in the FNP toolbox.
What types of documents can be analyzed with financial narrative processing?
A wide range.
Common sources include:
- Annual reports (especially sections like the Management Discussion and Analysis (MD&A)).
- Quarterly earnings call transcripts.
- Press releases.
- Investor presentations.
- News articles about companies.
- Internal memos or financial commentary.
Any text that discusses financial or business performance can be subject to FNP techniques.
Do I need specialized software or a data science team to use FNP?
While FNP started as a niche research area, it’s becoming more accessible. Now, tools and software (often part of analytics or business intelligence platforms) incorporate natural language processing for finance.
You don’t always need to code your own algorithms, either. Many solutions offer features like narrative trend dashboards or automated report summarization.
Of course, for very customized analysis, data science expertise can help, but the barrier to entry is lowering as AI capabilities are built into finance tools.
Can financial narrative processing handle multiple languages?
Yes, to an extent. Researchers have expanded FNP beyond English, as seen with projects in Spanish, Greek, and other languages. Many modern NLP models support multiple languages.
So, if your company operates in different regions, you can also apply FNP to non-English documents. You might need to use models or tools trained specifically for those languages, but the overall concept remains the same.
How Datarails Can Help
Financial narrative processing is changing how finance professionals interpret data. It allows you to leverage both the qualitative and quantitative aspects of financial information for a more complete picture.
If you’re interested in bringing these kinds of advanced intel into your own financial analysis process, get a Datarails demo today.
We’ll show you how our FP&A platform can help you make sense of both your numbers and the narratives.