Profit and Loss (P&L) statements, aka income statements, are one of the three primary financial statements. Alongside the balance sheet and cash flow statement, the P&L statement represents a company’s current financial position and its projected performance against current benchmarks.
The P&L statement contains revenues, costs, overheads and a net profit calculation. One brief review of a company’s P&L statement should quickly indicate whether it is profitable or not.
Though a P&L statement contains essential information, this information often exists among irrelevant information in a PDF. Analysts must comb data, copying the relevant data into the right format.
Handling P&L statements can be time-consuming, repetitive and ultimately unproductive for financial analysts. Recognising the dependence on manual, back-office processes, financial technology firms have developed groundbreaking solutions, specifically software for P&L statements. These tools build on foundational technologies like Optical Character Recognition (OCR), creating slick and continuously learning tools specialised for P&L statements.
For smaller companies in the UK, P&L statement software is likely to be of particular interest. The Economic Crime and Corporate Transparency Act 2023 requires smaller firms to declare their profit and loss accounts. Mandating the declaration of P&L figures ensures that the public can access key company information like turnover.
The Act may also motivate some companies to investigate technological shortcuts to lift and analyse P&L statement data (quickly). However, the world of financial statement software is constantly developing alongside newer technological innovation, such as advances in AI. In this article, we’ll cover the typical features and common FAQs about P&L statement tools. Let’s dive in.
Credit to the original template: wired.com
We’ll examine the features of specialised P&L statement tools rather than large language model (LLM)-based virtual assistants like ChatGPT. Whilst we’ve written extensively about how ChatGPT often fails to handle financial data, we don’t count an LLM as qualified P&L statement software.
The natural language algorithms that ChatGPT uses to comprehend language can be packaged into more sophisticated, specialised tools. Such tools can fulfil several essential data management functions for P&L data. And, unlike LLMs, they can execute these functions quickly and accurately.
Data must be accessible to create value. If the P&L data is in a PDF and you need to manipulate it in a spreadsheet, then it has no value. Data extraction will unlock the data by presenting it in the desired format, such as Excel, CSV, JSON and other formats.
Accurate data extraction hinges on the technology understanding the financial concepts presented on the P&L statement, rather than just copying the shape of the letters (cough OCR cough). AI-powered algorithms can match the intricacies of financial data where traditional, template-based data capture falls short. So after 'reading' the P&L statement, the extraction tool can populate an output file with the P&L data. In minutes - not hours - the financial data is ready to be downloaded.
Of course, if an AI engine understands financial concepts well enough to know which data to extract, it figures that it can also calculate financial ratios quickly. After all, calculating Gross Profit is simply subtracting the Cost of Goods Sold (COGS) from the net revenue. Therefore, once the data has been accurately extracted and validated, it’s quick and easy for AI to compute and present key financial ratios. Newer P&L tools can save time by calculating key ratios and validating the data to ensure accuracy. Like the extracted data, the structured financial data is ready to be downloaded quickly, saving minutes for overburdened analysts.
Reading the structured financial data should give a quick overview of the profit and loss statement. Examples of these financial ratios include:
An experienced analyst should be able to review these ratios, quickly gaining insights about the company's valuation, investment decisions, debt capacity, etc. These ratios should be exportable with the extracted data, meaning you can quickly compare the raw data with its structured, analysed counterpart.
In addition to being accessible, P&L data must be accurate to create value. AI-powered validation mechanisms will comb the data and affirm its consistency. For example, if the COGS doesn’t equal starting inventory (+) purchases (–) ending inventory, clearly something has gone wrong with extracting the data (or, even worse, preparing the data for the P&L statement). Again, data validation is only possible because AI understands what the financial data means. For example, if the value of the total operating expenses is higher than gross profit, trained AI will instinctively know - in the same way a trained analyst would - that an error has occurred during data collection.
Internal validation mechanisms ensure the data. However, external validation mechanisms might also link the P&L data to other sources, ensuring consistency in both datasets.
Data validation marks the difference between specialised P&L statement solutions and commercial LLMs. P&L software should be trained to identify and flag errors for manual correction. LLMs will not flag errors in extracted and analysed P&L data, requiring a thorough manual review.
The above are only three of the manifold capabilities of P&L statement software. Other software features might include visualising the P&L data (e.g. in a Sankey diagram), using P&L data for tax planning, etc.
A useful feature is value adjustment, where you can manually adjust the extracted values of a financial statement, and then log a reason. For example, you might adjust historical data for inflation, ensuring that this is logged alongside the change. Good P&L software should be founded around the 3 As - accountability, audiitability and accuracy.
How are you going to send and receive your P&L data? That’s what the integration method determines. There’s no ‘best’ method of integration. Rather, successful implementation will depend on your company’s pre-existing architecture. Some companies may prefer a lightweight integration method, such as a connective software like Workato, instead of a heavyweight integration via API.
A year ago, uploading PDFs or images to LLMs like ChatGPT was impossible. Today, the rapid advancements in AI and machine learning are driving significant improvements in software capabilities. As a result, you can anticipate your P&L statement software to become faster, more accurate, and more sophisticated.
Always select a vendor with an established reputation with dedicated resources for continuous improvement.
Our financial data project team often receives several frequently asked questions – let’s answer four of the most popular ones.
Many P&L statement tools can automatically classify documents, meaning they can identify the balance sheet and income statement from an annual or a quarterly report. In sum, users can upload long documents and just receive the P&L statement data.
There are several ways that P&L statement tools can keep financial data safe:
When financial data is transmitted between devices or over networks, it can be encrypted to prevent unauthorised access. Encrypting sensitive financial data is necessary for ensuring safety and privacy.
Common certifications in the industry include ISO 27001 and SOC 2.
A pen test is an (authorised & simulated) cyberattack designed to test the robustness of a system. Therefore, if you're especially concerned about your financial data's security, consider asking the vendor the date of their most recent pen test.
Two common pricing models for P&L statement tools include:
For those looking for predictable costs, you might opt for a subscription-style model. However, consider a per-page option if you don’t want to use the P&L tool consistently.
Some tools might require you to buy credit, so you only pay for the number of pages or documents you process. Tools with this pricing model may be more attractive for more casual users.
Ongoing industrial and academic advances in AI suggest that P&L statement software will become faster, more accurate and easier to access. Current AI research is cultivating AI’s ability to make context-appropriate decisions (read our collaboration with VentureBeat about this).
Better strategic and contextual decision-making means that AI could make recommendations based on the extracted and calculated data. For example, AI may be able to predict and model a company’s future financial performance and leverage the data to recommend how the company can reduce their overhead costs to maintain the best possible performance.
In other words, we may see AI fully recognise the significance of financial data. However, these capabilities are likely years down the line. In the meantime, the best way to deploy P&L statement software is by automating simple administrative and analytic functions, like extraction and calculation.
The best way to identify a best-fit solution is to try out various tools. Many P&L tools will offer demos or free trials to assess their usability, capabilities and robustness. Also, there are various online sample PDFs if you don’t want to use your P&L statements for testing.
So, without further ado, we invite you to try Financial Statements AI.
Financial Statements AI is our product for extracting data from financial statements. Its function is simple: to capture information from P&L statements and balance sheets and leverage the data to calculate financial ratios (e.g. EBITDA, OPEX, gross profit, and more). The extracted and processed data is then available to download as an Excel spreadsheet.
Some interesting facts about Financial Statements AI include:
To try Financial Statements AI for free, please book a demo or email hello@evolution.ai.
