How to Use NLG for Financial Reporting in the AI Era
NLG: The Financing Translator: Natural Language Generation simplifies complex loan data into clear sentences, enabling you to make informed decisions quickly.
Time-Savings: Automating financial reports with NLG means less manual labor and more accurate insights. This saves time and reduces human error, allowing finance teams to focus on strategic tasks.
Investor Clarity = Trust Boost: NLG enhances investor communications by translating complex financial performance into understandable narratives, minimizing misinterpretations and fostering trust.
Imagine you're a bank manager receiving a mountain of loan data daily. NLG turns these numbers into easy-to-read, high-quality sentences. For instance, it might say, "Last month, we approved 200 new loans, helping more people buy homes."
In finance, NLG makes data talk like a person, simplifying decision-making for bankers, executives, and others.
It’s more than a financial trend; this tech breakthrough can save time, analyze data, and enhance your reporting processes.
NLG in Finance
NLG is a technology that enables machines to convert structured data into human-readable natural language text.
It bridges the gap between raw data and comprehensible narratives. NLG gives machines the ability to tell a coherent and meaningful story using the available numbers, statistics, and financial data.
This capability holds immense value for the entire finance function, beyond just gathering data for financial reports.
Here are some current uses of the technology:
Automated Reporting
Natural Language Generation can automatically generate financial reports, summaries, and insights from complex data. This is useful for analysts and investors needing quick access to comprehensible summaries of financial performance, or for companies wanting to provide this to investors. Speaking of which…
Investor Communication
NLG can help companies communicate their financial results to investors and stakeholders clearly, in detail, and in a standardized manner, ensuring transparency and reducing misinterpretation risks.
Note from the Editor: Coming from an investor relations background, I can attest to the frequency of investor misinterpretations and their impact on stock prices. The clearer your reports, the less you have to worry about a disconnect between investor sentiment and company results.
Data Interpretation
Financial data can be overwhelming, with numerous variables and figures. NLG can distill this data into plain language, making it easier for decision-makers to understand trends, risks, and opportunities. This use case means fewer analysts are needed to interpret and share results with your executive team, resulting in direct cost savings for your department.
News Generation
Instead of requiring a team to track trends, technology can create automated financial news articles based on earnings reports, market trends, and economic indicators. This can benefit transparent public relations or financial news outlets, ensuring timely and consistent reporting.
Personalized Insights
Want to please important institutional investors or board members? NLG can tailor financial insights and recommendations for individual investors based on their portfolios and goals, providing a more personalized investment experience.
NLG in finance is like having a virtual financial analyst and reporter at your service, turning complex financial data into clear, concise, and actionable information. Offering this to your investors would build trust and brand loyalty!
How Does NLG Work?
The journey begins with tech-savvy finance professionals gathering extensive data, like financial statements, market data, or customer transaction records. This raw and intricate data makes it challenging to extract meaningful insights quickly… unless you’re a computer.
Step 1: Analysis
NLG starts by analyzing data, using algorithms and statistical techniques to discern patterns, trends, and outliers. For instance, it can identify a company's revenue grew by 15% last quarter or a stock had a significant price jump. Once NLG has sifted through the data and identified crucial information, it embarks on the narrative generation phase — where NLG shines.
Step 2: Narrative Generation
It translates insights into coherent narratives like reports, summaries, or explanations. For example, NLG might craft a report that reads, "Company XYZ experienced substantial growth last quarter, with a 15% revenue increase attributed to strong sales." The language is tailored for finance professionals and layman investors, removing the need to interpret complex data.
Personalizing Your NLG Tools
NLG doesn’t offer a universal solution; you can (and should) customize your tech to suit your needs. Define the type of reports or narratives you want, the detail level, and the language tone. NLG can operate on an automated schedule, producing reports daily, weekly, or as needed. This automation saves time and ensures consistency by reducing human error.
Step 3: Outbound Communication
The narratives are delivered to internal and external finance professionals for informed decision-making in management accounting or portfolio rebalancing. For example, a CFO could receive automated business unit performance summaries from NLG, enabling them to monitor organizational health more frequently and quickly.
Common Applications of NLG and AI
The financial sector is undergoing a significant transformation driven by Natural Language Generation and AI integration. These technologies are reshaping the industry by automating processes and enhancing decision-making. Key areas include:
- Automation of financial reports
- Decision-making
- Business intelligence
Automation of Financial Reports
A key application of NLG and AI in finance is automating financial reports. Compiling these reports traditionally involved extensive manual labor and data analysis, making it time-consuming and prone to human errors. NLG changes the game by autonomously extracting, analyzing, and translating data into coherent reports.
Imagine you’ve started a new, high-risk, high-spend business unit requiring daily progress reports. Instead of hiring an intern, NLG systems can handle the task, turning raw data into concise narratives highlighting trends, risks, and opportunities.
More accurate, consistent results in less time (and cost).
Decision Making
In finance, informed decision-making is crucial. NLG and AI provide 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 much faster than manual efforts.
You can use AI-powered predictive analytics software to forecast market trends. NLG then translates these forecasts into plain language narratives, explaining the implications for business decisions like price changes and geographic expansion.
Business Intelligence
Business Intelligence (BI) tools have long been essential in finance for data collection and analysis. However, NLG takes BI to the next level by adding a layer of human-like narrative generation. Instead of presenting charts and graphs, NLG can explain the story behind the data.
A BI dashboard might show an increase in sales for a 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 success drivers or areas needing 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, its capabilities can be amplified when integrated with other relevant tools. Sometimes, it's easy, like when financial reporting software comes with AI and NLG built in.
Whether pairing NLG with data visualization tools for intuitive dashboards or integrating it with CRM systems for personalized financial advice, the possibilities are numerous.
These are a few of the most common NLG integrations out there:
NLG + Process Automation
Process automation drives efficiency in finance. Coupled with NLG, it reduces manual workloads and errors. NLG can analyze raw data and craft narratives without human intervention. This saves time and ensures consistency in reporting, crucial in finance.
This means finance professionals can focus on higher-level tasks like strategy development and client interactions, while NLG handles data transformation and reporting. It's a win-win scenario, where automation and NLG empower each other to elevate financial operations.
NLG + Predictive Analytics
Predictive analytics—forecasting future trends based on historical data—is essential in finance. NLG and predictive analytics work together to convert 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 is speed and clarity of communication. By leveraging NLG to interpret complex predictive models, financial professionals can convey insights to non-finance team members in an easy-to-grasp way. This real-time, narrative-driven approach enhances trust and fosters better decision-making, while monitoring the fluctuating financial markets.
Next Step: Implementation
NLG is a powerful tool in finance, helping businesses improve operations and stakeholder communications while saving money. Now we just need to understand it enough to implement it!
In my next articles, I’ll provide a detailed guide on how to do this - if you don’t want to miss out, subscribe to The CFO Club’s newsletter and get it sent directly to your inbox.
