The rapid pace of advancement in generative AI technology adds a bit of complexity to the mix when creating a guide for using Natural Language Generation (NLG) for financial reporting… but luckily, I can update this article as new updates roll out.
In November 2022, ChatGPT was unveiled, and just four months later, OpenAI introduced GPT-4, a significantly enhanced large language model (LLM).
Furthermore, by May 2023, Anthropic's generative AI, Claude, demonstrated remarkable progress, capable of processing 100,000 text tokens per minute, equivalent to approximately 75,000 words – the length of an average novel.
This was a substantial leap from its initial capacity of around 9,000 tokens when it first debuted in March 2023. Additionally, in May 2023, Google made significant announcements regarding new features powered by generative AI, including the Search Generative Experience and the introduction of a new LLM named PaLM 2, which will drive various Google products, including the Bard chatbot.
Generative AI has found a significant application in the realm of finance, and one of its powerful use cases in this field is NLG.
NLG is like having a smart storyteller for financial data.
Imagine you're a bank manager, and every day, you receive a mountain of numbers about loans. That is a lot of data analysis needed! NLG takes these numbers and turns them into easy-to-read and high-quality sentences. For instance, it might say, "Last month, we approved 200 new loans, helping more people buy homes."
In finance, NLG is the magic that makes data talk like a person, making it simpler for bankers, executives, and everyone else to make smart decisions.
It’s more than just a financial trend; this technological breakthrough can save time, analyze the underlying data, and improve your reporting processes.
NLG in Finance
NLG is a technology that enables machines to transform structured data and information into human-readable natural language text.
In essence, it bridges the gap between raw data and comprehensible narratives. It's as if NLG gives machines the ability to tell a coherent and meaningful story using the numbers, statistics, and financial data at their disposal.
More than simply gathering data for financial reports, this capability holds immense value for the entire finance function.
Here are some of the current ways the technology can be used:
Natural Language Generation can automatically generate financial reports, summaries, and insights based on complex financial data. This is particularly useful for analysts and investors who need quick access to comprehensible summaries of financial performance or for companies wanting to provide this to their investors. Speaking of which…
NLG can help companies communicate their financial results to investors and stakeholders in a clear, detailed, and standardized manner. This ensures transparency and reduces the risk of misinterpretation.
Note from the Editor: Coming from an investor relations background, I can attest to the frequency of investor misinterpretations and how radical a stock price can be affected by this. The clearer your reports, the less you have to worry about a disconnect between investor sentiment and company results.
Financial data can be overwhelming, with numerous variables and figures to consider. NLG can distill this data into plain language, making it easier for decision-makers to understand trends, risks, and opportunities. In addition, this use case means fewer analysts are needed to interpret and share results with the rest of your executive team… aka, direct cost savings for your department.
Instead of necessitating a team to stay on top of trends, technology can create automated financial news articles based on earnings reports, market trends, and economic indicators. This can be especially useful for transparent public relations or financial news outlets, ensuring timely and consistent reporting.
Have some important institutional investors or board members you want to please? NLG can tailor financial insights and recommendations for individual investors based on their portfolios and goals, providing a more personalized investment experience.
In essence, NLG in finance is like having a virtual financial analyst and reporter at your service, capable of turning complex financial data into clear, concise, and actionable information. If you’re able to offer this to your investors, imagine how much more trust and brand loyalty you’ll create with them!
How Does NLG Work?
The journey begins with tech-literate finance professionals gathering an extensive amount of data, such as financial statements, market data, or customer transaction records. This data is often raw and intricate, making it challenging to extract meaningful insights quickly… unless you’re a computer.
Step 1: Analysis
NLG starts by analyzing this data, employing mathematical algorithms and statistical techniques to discern patterns, trends, and outliers within the data. For instance, it can identify that a company's revenue grew by 15% in the last quarter or that a particular stock had a significant price jump. Once NLG has sifted through the data and identified crucial information, it embarks on the narrative generation phase - this is really where NLG shines.
Step 2: Narrative Generation
It translates the identified insights into coherent and comprehensible narratives. These narratives can take the form of reports, summaries, or explanations. Using the example from Step 1, NLG might craft a report that reads, "Company XYZ experienced robust growth in the last quarter, with a 15% increase in revenue attributed to strong sales performance." The language used is tailored to be easily understood by finance professionals and layman investors alike, removing the need to interpret complex data themselves.
Personalizing your NLG Tools
NLG doesn’t exactly come with a one-size-fits-all solution; you can (and should) customize your tech to suit your specific needs. Define the type of reports or narratives you want to generate, the level of detail involved, and even the tone of the language. What’s better, NLG can operate on an automated schedule, producing reports daily, weekly, or as required. This automation saves time and ensures consistency in reporting by reducing the risk of human error.
Step 3: Outbound Communication
Finally, the generated narratives are delivered to internal and external finance professionals, who can use them for informed decision-making, whether that be for management accounting or portfolio rebalancing purposes. For example, a CFO could receive automated business unit performance summaries generated by NLG, enabling them to monitor organizational health more frequently, in less time.
Common Applications of NLG and Artificial Intelligence
The financial sector is experiencing a significant transformation driven by the integration of Natural Language Generation and AI. These technologies are reshaping the industry by automating various processes and enhancing decision-making capabilities. Among the numerous applications, a few key areas stand out prominently: Automation of Financial Reports, Decision Making, and Business Intelligence.
Automation of Financial Reports
One of the most tangible and impactful applications of NLG and AI in finance is the automation of financial reports. Traditionally, compiling financial reports involved extensive manual labor, sifting through mountains of data to create comprehensive documents. This was not only time-consuming but boring as heck and, therefore, prone to human errors. NLG changes the game by autonomously extracting, analyzing, and translating data into coherent and understandable reports.
Imagine a scenario where you’ve spun up a new, high-risk, high-spend business unit that requires daily reports on progress. Rather than hiring an intern and chaining them to their computer, NLG systems can effortlessly handle the task, turning raw data into concise narratives that highlight trends, risks, and opportunities.
More accurate, consistent results in a fraction of the time (and cost).
In finance, informed decision-making is paramount. NLG and AI lend a helping hand by providing real-time insights that empower financial professionals to make data-driven decisions. These technologies can sift through vast datasets, identify patterns, and present actionable information in a sliver of the time it’d take you to do it manually.
For instance, you can utilize AI-powered predictive analytics to forecast market trends. NLG then steps in to translate these forecasts into plain language narratives, explaining the implications for business decisions such as price changes, geographic expansion, etc.
Business Intelligence (BI) tools have long been essential in the finance sector for data collection and analysis. However, NLG takes BI to the next level by adding a layer of human-like narrative generation to the mix. Instead of merely presenting charts and graphs, NLG can explain the story behind the data.
For instance, a BI dashboard might show an increase in sales for a particular product. NLG complements this visualization by providing context: "Your company's sales increased by 20% last quarter due to a surge in demand for your flagship product." This narrative-driven approach enhances understanding and aids in identifying the factors driving success or areas that need attention.
Integration of NLG with Alternative Tools
The world of finance is multifaceted, and so are the tools used to navigate it. While NLG is a powerhouse on its own, its capabilities can be further amplified when integrated with other relevant tools. Think of it as an ensemble of financial instruments coming together for a symphony of efficiency and insights.
Whether it's pairing NLG with data visualization tools to create intuitive dashboards or combining it with Customer Relationship Management (CRM) systems for personalized financial advice, the integration possibilities are endless. By weaving NLG into your financial toolkit, you can effortlessly transform data-driven insights into compelling narratives that resonate with clients and stakeholders alike.
Synergy between NLG and Process Automation
Process automation is the engine that drives efficiency in finance. When coupled with NLG, it becomes a formidable force for reducing manual workloads and minimizing errors. NLG can take raw data, analyze it, and craft comprehensive narratives, all without human intervention. This not only saves time but ensures consistency in reporting, a crucial aspect in the financial world.
In practice, this means that finance professionals can focus on higher-level tasks, such as strategy development and client interactions, while NLG handles the heavy lifting of data transformation and reporting. It's a win-win scenario, where automation and NLG empower each other to elevate financial operations.
Interplay of NLG and Predictive Analytics
Predictive analytics, the art of forecasting future trends based on historical data, is indispensable in finance. NLG and predictive analytics make a dynamic duo by converting data-driven predictions into actionable narratives. For instance, a financial operator can use predictive analytics to forecast market trends, and NLG can translate these forecasts into plain language recommendations for product teams.
The key here is the speed and clarity of communication. By leveraging NLG to interpret complex predictive models, financial professionals can convey insights to non-finance team members in a way that's easy to grasp. This real-time, narrative-driven approach enhances trust and fosters better decision-making, all while keeping a finger on the pulse of the ever-fluctuating financial markets.
Next Step: Implementation
NLG is a very powerful tool to use in finance, helping businesses improve financial operations and stakeholder communications while actually saving money. The only thing to do now is understand it enough to implement it!
In my next few articles, I’ll give you a more detailed guide on how to do exactly this - if you don’t want to miss out on this, be sure to subscribe to The CFO Club’s newsletter and get it sent directly to your inbox.