Contract Analysis: How to effortlessly extract what matters most
Contract analysis has historically been a manual undertaking. It involves tediously reading large amounts of text to find specific information. The process consumes a significant amount of a lawyer’s billable hours, not to mention a client’s risk assessment expenses.
But what if there were an easier way for firms to extract only the information they need?
HighQ Contract Analysis uses machine learning technologies to make the expensive and tedious review process easier, faster, and more palatable. All management tasks can be done directly within the HighQ interface, making it a seamlessly integrated document processing pipeline.
Watch our HighQ Contract Analysis launch webinar to learn how to make your contract review process more efficient.
Why contract analysis matters
Contracts are put in place to guide lawyers what to do when a party fails to perform its obligations. If anything is missing or unclear, it could mean long legal battles and multiple interpretations. To mitigate these risks, lawyers look for key legal language, not just facts, in contract drafts to answer legal questions and flag deviations from standard language. However, it's a tedious, time-consuming, and human-intensive process.
While existing technologies already pinpoint key data like dates and parties, law firms are looking for even greater efficiencies with their contract reviews. More specifically, they want to shorten the process and, ultimately, the deal lifecycle. Using an “intelligent search” functionality allows them to extract various data from documents to create client reviews and reports, identify best practices, and support contract negotiation.
That’s what Contract Analysis from HighQ is designed to do: train machine learning models to understand what evidence they need to find in contracts to answer legal questions. “We are effectively bringing forward a solution that reduces the time it takes to get documents where they need to be,” said Steve Fullerton, Contract Analysis Product Manager, Thomson Reuters.
To read more on automated contract review and analysis, see our next articles “Understanding the complexity of automated contract review” and “Simplify the contract review process with machine learning”
Who benefits from AI-powered contract analysis?
Contract Analysis is a user-friendly, task-based review tool that aims to increase productivity across the firm. Whether you’re working in real estate, sales and services, intellectual property, employment, or commercial leases, junior lawyers can use it to identify relevant legal language within a contract to make risk assessment reports. Senior lawyers can use the artificial intelligence (AI) capabilities to ensure consistency when drafting client contracts. For firm partners, it’s a faster, more cost-effective process for identifying, analyzing, and reporting on their contracts.
What sets HighQ Contract Analysis apart?
With this platform, data is not just extracted and left to interpretation. AI-assisted, task-based guided reviews offer a logical order to how you answer questions. Contract analysis follows in the HighQ user experience of “no code, simple design, and high useability.”
“Our subject matter experts in Practical Law helped us with the language modelling, the structure of the documents, and how the defined terms and definitions are presented in different types of contracts,” said Fullerton. “Those key facts and legal language will answer the questions that have been drawn up on a topical or a legal provision basis to make the guided review process simpler.”
Here’s how it helps with the four stages of the review process:
1. Data integration. The integration between HighQ and Contract Analysis allows users to define what extracted information can be exported for use.
2. Classification. The AI Hub capability in the HighQ platform can classify and file documents of various formats for further processing by our machine learning models.
3. Text analysis. The guided review enables users to compare a contract under review to standard text and look for deviations or differences between the standard language and the contractual language. This level of analysis helps users understand context and identify risk more rapidly.
Reviewers can also make comments, assign risks, and report on progress. By default, the solution provides standard practical law, office, and sales and service agreements to help with this language comparison. But customers can also upload their own standard documents and share what comparative texts they want to use.
4. Workflow triage. In the final stage, all information is sent back to the AI Hub where users can set up downstream reports and make decisions based on data that the lawyer has verified and modified accordingly. The solution then shares this information with the powerful HighQ platform where it is put into workflows and visualizations so users can easily access, navigate, and make risk assessments.
“With Contract Analysis, we get an opportunity now to have a standard checklist, ask the questions to the document, and do the review work all in one platform,” notes Sebastian Bos, Director of Solution Consulting at Thomson Reuters. “[We] put the output into an iSheet, create the risk report using Contract Express, and use our dashboards and our workflows to get everything connected in those standard solutions.”
This new kind of user experience greatly enhances the overall proposition. The integration with HighQ also allows for seamless workflows, visualizations, and collaboration with team members at all levels of the firm. “There are lots of AI tools out there, but really what this does is cements an AI function with a platform,” added Ben Firth, Thomson Reuters Sales Management Lead.