Data extraction refers to capturing information and turning it into an actionable format. For example, you may convert a PDF scan of a contract into editable text. Two of the most common methods of data extraction are manual data entry and AI data extraction.
In this article, we’ll discuss the benefits, limitations and nuances of manual and AI-automated data capture so that you can extract data from contracts as accurately and efficiently as possible.
Before discussing how to extract information from a contract, it’s essential to consider what type of information you can extract.
Extracting metadata is a common use case for contracts. Contract metadata refers to information about the contract, such as:
Aside from data extraction, we can also refer to gathering metadata as data abstraction. Data abstraction aims to condense the contract into a simplified representation of its key information.
Of course, the requirements for any project data are unique. For instance, some projects may require data to be sampled directly from the contract, such as terms and conditions, penalty terms for non-compliance, etc.
Manual data extraction may seem like the most intuitive option. After all, contracts contain complex legal language, so using a legal professional to decipher that legalese (during transcription) makes sense.
However, many AI tools can now comprehend information from contracts in the same way as a human. Finely tuned Natural Language Processing (NLP) can detect subtle lexical nuances in contracts. For example, developers can train AI to understand (as humans instinctively would) the difference between obligations and permissions or references to external documents within the contracts.
But what are the advantages and disadvantages of each option? Let's delve into the pros and cons of manual and AI data extraction by examining data capture from non-disclosure agreements (NDA).
An NDA is a contract between parties that protects sensitive or confidential information. NDAs are commonplace in business transactions or investment contexts.
Given an NDA’s lexical complexities, employees might spend minutes to even hours scrutinising them for necessary information. For example, a legal department head may need to extract the definition of confidential data from several NDAs to ensure that employees appropriately handle the project’s data.
Other examples of data you may wish to extract from NDAs include:
You can then deploy the extracted data for downstream processing. Case in point – one of our contract extraction projects involved extracting the clauses for one of our client’s NDAs from each party’s copy and comparing them.
Any mismatch between the captured clauses would have indicated that someone had altered them. As for the fallout? Those alterations could have changed the meaning of the agreements, exposing both parties to unforeseen risks and legal complications. Hence, AI extraction from contracts can safeguard the fidelity of contractual agreements — while saving human labour.
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Of course, it may be tempting to start uploading contracts to publicly available large language models (LLMs) for extraction and analysis. However, giving LLMs sensitive data demonstrates poor data security practices for multiple reasons.
First, the LLM provider may retain the data for training purposes or fail to anonymise it adequately. Even with strong data protection assurances from the Large Language Model (LLM) provider, operational vulnerabilities or policy oversights can still lead to breaches of user contracts if sensitive data is exposed.
Let’s say these security concerns ceased to exist. Then, it’s worth considering whether LLMs are effective. As our investigations have shown, LLMs like ChatGPT can generate serious errors. The precise wording inherent in contracts means that misinterpretation by LLMs can cause serious consequences if not caught and corrected. Though the speed and cost-effectiveness of LLMs may seem attractive, there is a significant price to pay.
Using a specialist vendor with AI trained to extract information from contracts will likely prove a better alternative. Let’s dive in and explore how to navigate the complexities of using AI tools to extract data from contracts.
Despite extraordinary limitations, manual data extraction has advantages. An attentive, well-informed human can easily read, understand, and enter pertinent data from contracts into the desired repository.
AI developers like ourselves aim to replicate – and improve – a human’s ability to read contracts. After a day of reading contracts, however, a legal consultant or department head may experience fatigue, which could lead to cutting corners while transcribing data. In contrast, an AI system can learn from its errors and transcribe data more accurately than it did previously.
In a nutshell, AI continually self-improves. So, a key question to ask a potential vendor is how many contracts they’ve used to train their AI. Ideally, they’ll have a number ready, preferably spanning hundreds of thousands to millions.
Another key variable when deploying AI tools is balancing accuracy with speed. Among current AI models, there is a trade-off between the two. Consider whether you would rather receive your contract data:
The right answer will depend on your company’s unique requirements. We’d recommend speaking to a vendor for more information.
Manual data extraction from contracts has long been the norm in most industries. However, AI-powered data extraction tools offer a faster and more accurate use case for contracts. When engaging with a data extraction vendor, it’s important to have clear expectations about the desired performance.
If you would like to discover more about data extraction from contracts, a member of our financial data team would be happy to help. Book a demo, or email hello@evolution.ai for more information.
