Legal document data extraction refers to capturing the relevant information from a legal document into an actionable format.
For example, you might extract key information from a contract (i.e. its term, type and parties involved) into your internal database. Previously, firms expected legal interns to extract data from legal documents, but superior technological alternatives are emerging, spearheaded by AI.
Generative AI operates Natural Language Processing (NLP) to ‘read’ legal documents like a human. A human understands how terms, subject matter and party information interact in a legal document. Now AI does, too.
Traditional automation technologies (such as Robotic Process Automation or other rule-based technologies) lack the nuanced data extraction capabilities that AI can provide. Instead of intelligently parsing the document’s content, traditional data extraction technologies blindly copy information based on preconceived templates.
The Financial Times addressed the generative AI rollout in the legal industry in May. They summarised the benefits of automating everyday tasks with AI simply as ‘cut[ting] costs and increas[ing] productivity’ – goals that most firms are familiar with. By automating manual processes, not only can firms reduce costs but they can also create a sustainable foundation for increased profitability.
Another benefit of using an AI system to extract data from legal documents is that you can then leverage the extracted data to unlock further insights. Examples of these post-processing features include:
Classification
After extraction, the system might classify the document (such as an employment contract or a non-disclosure agreement) and send it to the desired repository (such as a shared repository).
Visualisation
The system may visualise case data (e.g. case status and time spent) into digestible visual representations.
Document Automation
Some systems may be able to implant the extracted data into new documents (a process known as document automation).
System Integration
You can integrate the data extraction system into case management software. Popular case management software like Access Group’s products or LEAP allows users to track the lifecycle of cases and manage documents. By inputting extracted data into a case management system, you can maintain your data’s transparency and accessibility.
These post-processing functionalities work alongside the features of typical legal document data extraction technology to deliver swift, accurate data extraction.
Not all data extraction solutions are built equal. Each legal document capture technology will have different features, depending on its production and deployment.
Users can quickly search through the indexed and sorted document data by entering all extracted data into an internal system. For example, legal professionals could expedite their research by searching for relevant case law, statutes and other required legal resources.
Contract metadata can be an invaluable asset. Rather than simply copying the information from the contract into a different format, contract metadata offers key information about the contract. Examples of contract metadata include:
Contract metadata can then be stored for later review.
Extracting information from legal documents may only be the first step, depending on your company’s requirements. Connecting legal data extraction platforms to other systems can help your company create intelligent automation workflows.
To prevent repeated calls or emails to your firm’s IT department, check that the legal data document software offers easy integration. Though you could use the platform online, you might also consider connecting via REST API or an integration platform like Workato.
The following is a basic overview of how data extraction platforms generally work.
Upload the legal document. Most platforms should accommodate any format (PDF, PNG, JPEG, Word, etc.).
The platform will extract the required data safely and securely in seconds. You can then review the extracted data. If there are any mistakes or errors in the extraction process, the platform should flag them.
If the data extraction software is part of an automated workflow, the extracted data will be sent to the desired repository (e.g. an internal database) immediately.
Certainly, it would be a mistake to assume that ChatGPT or a similar large language model (LLM) offers the same security as a specialised solution. These AI models produce hallucinations – fictitious information that sounds plausible.
In several cases, lawyers are on record using AI in submitted pleadings. Here are two notable examples:
As Thomson Reuters identified, these lawyers used AI because of the pressure from litigation (anointing generative AI as the seeming saviour for cutting time and costs).
Yet, in these cases, there was a strong lack of accountability for using AI-generated case law. Users attempted to blame legal interns, deny knowledge about how AI solutions worked or deny using AI at all.
Such dishonesty can create problematic court evidence. When AI produces hallucinations, it can be extremely difficult to distinguish between what is from genuine case law and what is a hallucination (though there are small giveaways).
There are ways to harness AI’s convenience without ending up in Bloomberg Law News. LLMs like ChatGPT often struggle with legal documents due to their limited training data. By using specialised data extraction tools designed with safeguards and validation mechanisms, you can eliminate the risk of encountering fictitious information. The result? Accuracy under all conditions.
Ideally, a data extraction platform should offer accuracy under changing conditions, including:
Uploading different types of legal documents
If you need to extract from different types of legal documents - such as contracts and evidence documents for group litigation orders - the data extraction platform should consistently generate completely accurate data.
Uploading in volume
When tested, many leading data extraction tools freeze or significantly slow down when users upload lengthy documents or bulk uploads.
Uploading large volumes of data should not directly compromise accuracy. However, if data extraction tools freeze or significantly slow down, it indicates a concerning lack of resilience.
Uploading poor-quality scans
If you need to extract from historical data, you might have to deal with low-quality scans, potentially containing handwriting or missing information. We found when working with LitFin that AI-powered data extraction tools can maintain outstanding levels of accuracy, without compromising speed. Let’s dive in.
LitFin is a litigation funder with extensive experience in complex European competition law. The company extracts information from thousands of evidence documents annually to process claims for group litigation orders (GLOs).
To extract data from these documents manually would require an extensive workforce of data entry operators. The firm opted for a more technologically adept approach – employing AI to ‘read’ the evidence documents like a human.
We worked with them to extract data from poor-quality scans of Germanic invoices. LitFin received extracted data from 300,000 invoices in just a few weeks.
Less well-calibrated AI wouldn’t have been able to translate the information into machine-readable text. We continue to work with LitFin today for all their extraction requirements.
Read the full case study here.
If your legal firm is currently exploring legal document data extraction tools, we would like to hear from you.
Evolution AI has worked with leading legal firms like Mishcon de Reya and LitFin to automate data extraction from legal and financial documents. To discover more about our sophisticated AI technology, contact our financial data project team by emailing [email protected] or booking a demo.
