Managing and reviewing contracts throughout their lifecycle is a difficult task for companies, especially since contractual data is often scattered across different systems or departments, making it difficult to obtain a quick and complete overview of contractual obligations.
Consider the volume of contracts that companies typically handle, the effort required to manually review dense, unstructured legal information, and the (legal) expertise needed to interpret the data within the contracts.
It's easy to see why managing contracts can become extremely challenging!
Contract data extraction solutions can help address some of these key challenges by:
- Reduce time spent manually reviewing contracts
- Provide relatively faster access to critical contractual information.
- Enabling proactive management of contractual obligations and deadlines
In this article, we will learn more about contract data extraction, challenges in contract data extraction, some popular contract data extraction methods, and find out how you can streamline various stages of the contract lifecycle.
Contract data extraction is the process of automatically identifying and extracting specific/relevant information from contracts or legal documents.
This process transforms unstructured contract text into structured data that is much easier to analyze. This also helps businesses find and use key details hidden in their contracts, making it easier to understand and manage their agreements.
Below are some use cases that focus primarily on contract analysis along with examples of key contractual data:
| Use cases that require contract analysis | Key contract data to be extracted |
|---|---|
| 1. Merger and acquisition | Names of parties, contract values, termination clauses, change of control provisions, etc. |
| 2. Supplier management | Pricing terms, renewal dates, service level agreements (SLAs), liability clauses, etc. |
| 3. Lease Administration | Lease terms, rent amounts, renewal options, maintenance responsibilities, etc. |
| 4. Employment contracts | Compensation details, non-compete clauses, benefits information, termination conditions, etc. |
Why is it difficult to capture contract data?
Given the legal nature of contracts, a high degree of accuracy is extremely crucial, leaving very little room for error.
But no contractual data extraction solution, even automated or ai-driven ones, can guarantee 100% accuracy in data extraction!
Here are some reasons why:
- Contracts, like most business documents, come in many different formats, designs, and structures.
- Legal documents and contracts often use complex language, industry-specific terminology, and ambiguous legal jargon.
- Different organizations may use different terms or context-dependent information to describe the same concepts.
Despite the challenges addressed above, contract data extraction solutions (especially automated ones) are increasingly being adopted by companies looking to move away from manual contract reviews.
These solutions leverage a combination of natural language processing, legal expertise, and artificial intelligence to read and understand contracts and identify key data within them. These tools can be grouped into two types:
- Specialized LLMs trained in legal data such as Harvey ai or Robin ai which are primarily used for legal review and contract analysis
- ai-powered rules-based Intelligent Document Processing (IDP) solutions, such as Nanonets, are primarily used to automate existing contractual data extraction workflows
Most LLMs and generative ai-based solutions are prone to hallucinations, especially when they encounter unknown data.
That's why you can't use GPT Chat or Claude with absolute certainty for legal reviews or contract analysis.
On the other hand, LLMs trained in legal data and case law materials have a deeper and better understanding of legal terminology and contractual structures, and are less likely to hallucinate or make things up.
Since these LLMs are trained on large legal data sets, they have excellent contextual understanding. They can even understand clauses within the broader context of a contract.
They are ideal for contract analysis, legal research, and drafting legal documents, saving time that would otherwise be spent on manual research. Below are some examples of the best LLMs trained in contract review software using legal data or artificial intelligence:
- Harvey's artificial intelligence:A legal-focused ai using GPT technology
- Robin ai:A co-pilot for legal tasks
- LEGAL-BERT: TO BERTai-based machine learning model trained on hundreds of thousands of legal documents
- Lexis+ artificial intelligence:A personalized artificial intelligence legal assistant
- Casetext Associate Attorney:An ai legal assistant powered by GPT-4
1. Significantly reduces time spent on contract review and data extraction.
2. Handles multiple types and formats of contracts more effectively than rules-based systems.
3. Identify patterns and insights across large portfolios of contracts.
4. Create searchable and shareable databases of contractual information across teams and departments.
1. It has the potential for misinterpretation, especially with complex or unusual clauses that have not been encountered before.
2. It takes time and experience to implement and properly tune to maintain accuracy.
3. May not integrate seamlessly with existing contract management systems and workflows.
4. High initial investment for licensing, implementation and ongoing maintenance
Below is a generic tutorial on how to use LLMs trained on legal data, such as Harvey ai or Robin ai, to extract contract data:
- Make sure the contract is in a machine-readable digital format (e.g. PDF, Word, or plain text).
- Identify the specific data points you need to extract (e.g. parts, dates, terms, clauses) and specify a structured format for the output (e.g. JSON, CSV).
- Create and adjust prompts that tell the LLM to extract specific data. For example: “Extract the following information from this contract:
- Parties involved
- Contract start date
- Contract end date
- Terms of payment
- “Termination clauses”
- Enter the contract text and your instructions into the LLM. Some platforms may offer an API for this step!
Be on the lookout for missing or incorrectly extracted information.
- Use the results to further refine your directions and improve accuracy.
Handling such exceptions may require custom guidance (for these unique contracts only) or sending them to manual review.
More often than not, companies looking for a contract data extraction solution require something that can fit into their existing setup or workflows.
Ideally, no one would prefer a solution that requires abandoning an existing contract management system or making a lot of modifications to existing processes.
Rule-based IDP solutions are very useful for automating contractual data extraction workflows without disrupting existing processes. They serve as an ideal intermediary between unstructured contracts and contract management systems (or legal ERPs).
1. Produces consistent structured data output – no brainer!
2. Integrates with existing contract management systems and feeds extracted data directly into other business processes.
3. Handles different types of documents beyond contracts: Can be used for a wider range of business use cases.
4. It is much easier to train or improve models to handle exceptions or special cases.
1. You have difficulty with complex legal language or “invisible” contractual formats that require in-depth legal analysis.
2. Does not generate summaries or cannot explain the terms of the contract.
Here is a quick guide on how to use Nanonets, a popular ai-based data processing software, to extract contract data. In this example, we will extract data from a commercial lease agreement.
- Sign up for Nanonets, log in to your account, click “New Workflow” and create a “Zero Training Model”.
- Specify the data points you want to extract from your lease. For example, these are the data points I want to extract from a sample commercial lease:
- Owner
- Tenant
- Owner's address
- Tenant's address
- Start date
- Completion date
- Upload your contract and wait a few seconds. Nanonets ai will display key contract data as follows:
- You can correct or modify the data extracted by the ai and it will “learn” from those corrections/modifications and continue to improve.
IDP solutions like Nanonets also enable you to build end-to-end automated workflows on top of robust data extraction capabilities. You can:
- Automatic capture of incoming contracts via email, active folders or API
- Refine extracted data using custom data actions
- Customize the final structured output
- Set up approvals or validations for data extracted from the contract.
- and finally export it to a subsequent contract management software or ERP
Here is a quick overview of these features in Nanonets:
