An income statement (also known as a profit and loss or P&L statement) is one of the three key financial statements. Alongside the balance sheet and cash flow statement, it offers a detailed breakdown of a company’s financial performance.
However, income statements also contain information that may not interest all readers, such as gains and losses from asset sales. They also contain speculative data (e.g. depreciation and bad debt expense). The analyst’s role is to cut through the noise and identify key insights from these financial documents.
Yet, the initial stages of such analysis involve data aggregation and presentation – tedious tasks for any analyst. In contrast, these processes are quick and easy for AI-powered income statement software. Let’s explore what income statement analysis software can do and how to access it.
While not all income statement analysis software offers the same features, here are some common functionalities.
The first step in analysing income statement data is accessing it. Since information in a PDF cannot be manipulated, income statement software will convert the data into Excel format (or CSV, JSON, etc.).
But how can AI convert information into Excel? Extraction algorithms parse the document, ‘reading’ using a neural network architecture similar to the human brain. The algorithms then organise the captured data in the requested format. Overall, extracting income statement data is essential in eliminating all unwanted data from an income statement.
Manually calculating financial ratios from the line items on an income statement isn’t difficult. For example, adding depreciation and amortisation expenses to operating profit (EBIT) to calculate EBITDA is a quick sum. However, repeatedly calculating these ratios is boring for analysts and can introduce errors into the dataset.
After accurately extracting key financial data, intelligent algorithms will put the final pieces of the puzzle together to calculate financial ratios in seconds. For a typical analyst, these seconds represent hours of work annually.
New Large Language Model (LLM)-based technologies can answer simple questions about income statements. For example, you might upload an income statement to an LLM like ChatGPT and prompt it with something like:
While chatbot technologies specifically designed for financial documents are currently under development, we can attribute their nascent status to underlying technological challenges. These limitations often result in inaccurate performance from chatbots.
When we’ve tested AI-powered financial tools, their Optical Character Recognition (OCR) and general processing capabilities generate approximately one error per page. Often, the LLM will generate information that sounds plausible (i.e. ‘hallucinations’), such as generating financial terminology that doesn’t exist in the document. For example, we’ve found when testing LLMs that they can generate specious income statement line items like sales forecasts or budgeted expenses.
From the user’s perspective, it’s necessary to fact-check the chatbot’s responses, which might take longer than manually reviewing the income statement (!). If you regularly work with income statements, you might consider timing how long it takes to analyse or process one before testing any technology designed to expedite the process.
While income statement analysis software can expertly handle financial data, there are two particular points of interest for potential users: flexibly structuring unstructured data and successfully integrating it with other financial software.
An income statement’s structure can be highly variable.
Firstly, international accounting standards like IRFS and GAAP require presenting income statements differently. Using tools to standardise income statement data can facilitate cross-border comparisons. Similarly, comparing different companies’ income statements can help to establish industry benchmarks.
The unstructured nature of income statements requires a flexible and creative approach, which older automation technologies cannot provide. Instead of regarding income statements through predetermined rules, income statement software must take an approach more akin to human cognition.
AI algorithms that mimic human reasoning can easily classify income statements from similar financial documents. These algorithms can then convert unstructured data to structured data for further analysis. If the structure of the outputted data needs to change – for example, if accounting standards change – AI-powered income statement software possesses the flexibility to adapt, mirroring the flexibility of a human analyst.
That means no need for extensive retraining of the model, no need to pay for a new model and no irritating and time-consuming post-processing. Instead, AI’s flexibility can strip away the hassle from these evolving financial processes.
Rather than using income statement analysis software as an isolated third-party platform, you might consider integrating it with other financial tools. Examples of these financial tools include:
Income statement software is often easy to integrate using APIs or integration tools like Workato.
Adaptable integration is essential for ensuring fair access. Income statement software that isn’t easily accessible by a variety of users is inherently undemocratic. Both independent financial advisors (IFAs) and analysts in enterprise-scale organisations can benefit from the time and cost savings borne by high-performance income statement software.
Financial Statements AI is our income statement extraction and analysis software. Let’s examine how to use Financial Statements AI to analyse income statements. Here’s how it works.
You can upload the income statement in any format, including an image or PDF. Wait until the green tag indicates that the income statement has been successfully uploaded.
If the income statement is contained within a larger document (like an annual or quarterly report), the income statement will be classified automatically. By switching between the ‘Extracted Data’ and ‘Processed Data’ tabs, you can manually review the data. Key ratios like Gross Profit Margin, ‘Operating Profit Margin’ and ‘Net Profit Margin’ are easily accessible in the ‘Processed Data’ tab.
Click the download button, and a spreadsheet containing both the extracted and analysed data will be downloaded to your device. You can then amend the data as necessary.
It really is straightforward to analyse income statements using AI-based income statement analysis software.
If you’d like to shape the development of Financial Statements AI (without cost) and try analysing income statements for yourself, book a demo or email hello@evolution.ai.
