AI's New Job: Reading Contracts to Predict a Company's Money Future

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AI's New Job: Reading Contracts to Predict a Company's Money Future Figuring out how much money a company actually makes, or its "revenue," is a really big deal. Everyone from the company's bosses and employees to government watchdogs and people investing their money pays close attention to it. How a company counts its revenue is largely based on the agreements it signs with customers or suppliers – these are called supply contracts. These contracts contain all the important details that decide how much money a company should report. With newer accounting rules, like something called ASC 606, these contracts have become even more central to the process of recognizing revenue. But here's the tricky part: understanding exactly how all the words and clauses in these contracts turn into reported revenue has always been tough. Why? Because supply contracts are often very long, full of dense legal jargon, and complex. The information isn't neatly organized; it's often just plain text, unstructured, and heavily depends on the specific situation and context of the agreement. Getting it right usually needs people who understand both business and law, plus detailed knowledge about the specific company and what it sells. Because of these difficulties, it's been hard for researchers to clearly show how the specific things within contracts relate directly to the revenue numbers a company reports. This is Where Artificial Intelligence Steps In Think of AI, especially the newer versions like Generative AI (GAI) tools such as ChatGPT, as super-smart readers that can handle mountains of text. These tools are particularly good for looking at documents like supply contracts because they can: • Handle huge amounts of complicated text. • Figure out the detailed connections and patterns hidden inside documents. • Remember and use the surrounding information (the context) even in very long agreements. • Access a vast amount of knowledge, including business laws, accounting rules, and how different industries work – which is vital for understanding contracts correctly. Word on the street is that even major accounting firms are starting to use GAI to help them analyze supply contracts when they audit companies. A recent study put GAI, specifically a powerful version called ChatGPT-4o, to the test. They used it to look at thousands of important supply contracts that public companies had filed with the government (the SEC) over more than 20 years. These contracts were chosen because rules say they should have a significant impact on the company's revenue. The main goal was to use AI to pull out valuable information from these contracts and connect it to the company's reported revenue numbers. How AI Unpacks the Contract Puzzle To deal with how complicated and varied supply contracts are, the researchers developed a smart approach using GAI: 1. Creating a Standard Map: First, the AI looked at the table of contents sections in many contracts to find a common layout. This helped identify 17 different types of contract sections, like "What's Being Sold," "How and When to Pay," or "What Happens if Someone Breaks the Agreement." This standardized map helps organize all the different kinds of information found across many contracts. 2. Deep Dive with Step-by-Step Thinking: Then, the AI was given the entire text of each contract, along with the standard map of sections. Using a technique that mimics human reasoning, the AI was guided step-by-step through the analysis. This process involved: ◦ Pinpointing basic facts, like the total value of the contract and how long it lasts. ◦ Estimating the expected revenue from the contract, including a best guess and a possible range, often relative to the contract's total value. ◦ Identifying which of the 17 standard sections were present in the contract. ◦ For each relevant section, the AI assessed its purpose, whether it likely increases, decreases, or has no effect on the expected revenue estimate, and how much it contributes to how uncertain the revenue recognition is or how much flexibility managers might have in reporting that revenue. The AI's ability to use the full contract text helps it accurately understand each section. What AI Found Inside Supply Contracts The AI's analysis of the contracts revealed some interesting things about how they relate to revenue: • Common Sections: While not every contract has every section, many appear frequently, such as sections defining terms, describing the product, or outlining payment terms (found in almost all contracts). Sections on warranties and what happens if the contract ends are also very common. • Sections Important for Revenue: The AI rated sections about "Product Specification" and "Purchase Price and Payment Terms" as the most important for recognizing revenue. Other key sections include those about closing deals, warranties, who pays if there are problems (indemnification), and contract termination. This lines up with the parts of contracts companies often ask to keep secret in their government filings, suggesting these parts are indeed seen as highly important. • Impact on Expected Money: Surprisingly, the AI estimated that, on average, only about 65% of the total value of a contract is expected to turn into recognized revenue. While the product and payment sections generally boost expected revenue, most other sections tend to lower it, likely because they include terms about things that could go wrong or reduce the final amount received, like termination clauses or unforeseen events. • Sources of Uncertainty: The AI found that there's quite a bit of uncertainty in recognizing revenue from these contracts. On average, expected revenues could swing by as much as 16% of the total contract value. This uncertainty is particularly tied to sections about "Product Specification," "Purchase Price and Payment Terms," "Closing and Conditions Precedent," "Representations and Warranties," "Indemnification," and "Termination and Remedies." • Manager Flexibility: Because contracts can't cover absolutely everything that might happen in the future, they can sometimes allow company managers some flexibility (discretion) in how they report revenue. The AI's assessment showed that this overall flexibility in a contract is influenced by the flexibility allowed in individual sections. Sections like "Purchase Price and Payment Terms," "Product Specification," "Closing and Conditions Precedent," "Warranties," "Business Conduct," "Indemnifications," and "Termination" were identified as giving managers the most room to maneuver. Using AI-Scanned Data to Predict the Future Beyond just understanding the link between contracts and revenue, the study also wanted to see if the information AI pulled out could help predict future revenue outcomes. They used another AI technique called Machine Learning (ML) for this prediction part, comparing the forecasting power of different types of information: 1. Standard Financial Data: Information typically found in a company's financial reports and other basic company details. 2. AI-Extracted Contract Data: The detailed information the AI pulled from the supply contracts. 3. Both Types Combined: Using both financial and contract information together. The ML models were trained to predict two things during the period a contract was active: • Actual Reported Revenue Growth: The result? The model using only the AI-extracted contract information was significantly better at predicting actual reported revenue growth than the model using just standard financial data. Adding financial data to the contract information didn't really make the predictions much better. This strongly suggests that the detailed information inside contracts is more useful for predicting future reported revenue than the numbers you typically see in financial statements. • Revenue Recognition Problems: The AI-extracted contract features were also much better at predicting potential problems related to how revenue was recognized (like having to correct past financial reports or getting questioned by regulators). Models using contract features were far more accurate in identifying actual issues compared to models using only financial data or a mix. This further emphasizes that details hidden within supply contracts are valuable clues for anticipating potential accounting troubles. The Bottom Line This important study shows that advanced AI tools can successfully understand complex legal documents like supply contracts and pull out information that is crucial for understanding revenue. The AI's analysis gives us valuable insights into the typical layout of these contracts, which sections are most important, and how they affect expected revenue, uncertainty, and managerial flexibility in reporting. Most importantly, the information the AI distilled from these contracts turned out to be powerful for predicting both future reported revenues and potential accounting issues related to revenue. This contract-based data actually performed better in predictions than traditional financial information. These findings are particularly relevant now, as current accounting rules shine a spotlight on the importance of contracts. By showing how AI can connect the dots between the fine print in legal documents and the numbers on a company's financial statements, this research offers significant value for businesses, auditors, regulators, and investors who want a deeper, more accurate picture of a company's revenue and financial health. Using AI to unlock the information within contract language provides a potent new method for discovering insights previously hidden away and improving financial forecasting and risk assessment.