Every legal counsel knows the drill—contract review is essential but often tedious. It's a balancing act between thoroughness and efficiency.

But what if you could have both without compromise?

That's the promise of Machine Learning in contract review. It offers:

• Speed without sacrificing accuracy,
• The ability to handle bulk contracts effortlessly,
• A new level of risk assessment precision.

This isn't just an upgrade; it's a complete overhaul of the contract review process, empowering you to perform at your best.

In this post, we'll explore how Machine Learning (ML) has revolutionized the contract review process, allowing legal professionals to not just complete tasks ten times faster but also afford the bandwidth for more valuable contributions.

What is machine learning contract review?

Machine Learning (ML) contract review refers to the use of algorithms, Natural Language Processing (NLP) techniques, and large contract datasets to streamline contract review processes.

ML is a component of Artificial Intelligence (AI) focusing on building systems that incrementally learn from data, identify patterns, and execute tasks in accordance with their knowledge base.

For contract review, this entails training software systems on a vast collection of real-life contracts and associated data, allowing them to learn patterns, extract relevant information, identify inconsistencies, and make accurate predictions.

This is demonstrated in SpotDraft’s VerifAI, a Microsoft Word add-in designed to help in-house counsel reduce hours and days of contract review into minutes.

Trained on a vast collection of contract data and prompts from the collective knowledge of top legal practitioners, VerifAI can instantly identify areas of non-compliance and potential risks, suggest the right modifications, and provide human-like answers to open-ended questions.

‍Feel free to check it out here.

Also read: How AI Contract Review Tools are Transforming Legal Workflows

How does machine learning contract review work?

“If you try to read a complex contract carefully, from front to back, and expect to understand it on just the first read-through, that’s wishful thinking (and potentially very messy).”
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~ Sterling Miller, CEO and Senior Counsel, Hilgers Graben PLLC
Ten Things: How to Read a Contract

A Machine Learning contract review tool is trained on a dataset of annotated contracts, where human reviewers have labeled specific clauses, terms, and potential risks associated with various elements.

But what really happens behind the scenes when you feed your contract into an ML tool for review?

Here’s a high-level overview.

#1 Data ingestion

The process begins with the contract review tool ingesting the contract provided by the in-house counsel. Documents may be in diverse formats, including PDF, Word, or other common file types.

“The more data [a Machine Learning contract review tool] has, the more it can refine its actions. This capability is vitally beneficial when dealing with the nuances of contract reviews.”
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~ Steve Fullerton, Product Manager, Thomson Reuters
Simplify the contract review process with machine learning

#2 Text extraction

This involves converting the contractual documents into a format machine learning algorithms can process.

In cases where images or scanned documents are used, the tool may use technologies like Optical Character Recognition (OCR) to extract the required textual content.

#3 Preprocessing

Here, the extracted text undergoes a preprocessing phase to enhance its quality and standardize the data. This includes removing irrelevant formatting, correcting errors, and ensuring consistency in the representation of the text.

The goal is to prepare a clean and standardized dataset for subsequent analysis.

#4 Tokenization

Tokenization is the process of breaking down the document's text into smaller units, known as tokens. These tokens could be words, phrases, or characters, depending on the desired level of granularity.

Tokenization facilitates the subsequent analysis by providing a structured representation of the document's content.

#5 Name Entity Recognition (NER)

NER is applied to identify and categorize entities within the text. This includes recognizing the names of parties involved, relevant dates, addresses, and other critical information pertinent to the contract.

#6 Language understanding

Utilizing advanced NLP techniques, the Machine Learning contract review tool parses sentences, comprehends contextual nuances, and identifies the semantics of the language used in the contract.

This encompasses syntactic and semantic analysis, as well as recognizing legal terminology and language conventions.

#7 Feature extraction

Features are extracted from the preprocessed text to create a structured representation of the document. This involves identifying and isolating key terms, clauses, obligations, and other relevant elements.

The goal is to create a feature-rich dataset for the subsequent application of machine learning algorithms.

#8 Classification and analysis

The ML model at the core of the contract review tool classifies different sections of the contract based on learned patterns from its knowledge base.

At this phase, it will categorize clauses, identify potential risks, and extract specific information such as payment terms, obligations, or termination clauses.

#9 Risk assessment

The tool performs a detailed risk assessment, evaluating various clauses and aspects of the contract. It flags potential issues, discrepancies, or areas requiring special attention from the legal team.

#10 Continuous learning and improvement

As it processes more contracts and encounters diverse scenarios, the machine learning contract review tool continually refines its algorithms and updates its knowledge base.

This involves learning from feedback provided by legal experts, incorporating new legal precedents, and adapting to changes in regulations. Continuous learning ensures that the tool evolves over time, becoming more accurate, efficient, and attuned to the dynamic nature of legal landscapes.

Also read: The Perfect Contract Review Checklist for Commercial Contracts

Benefits of machine learning contract review

The traditional approach to contract review has so many pain points that make it unfit for the modern business landscape. Its time-consuming and labor-intensive nature exposes in-house counsel to burnout, limits their ability to focus on more valuable aspects of their responsibility, and elevates the risks of errors and misinterpretation.

In the rapidly evolving business landscape, where agility is critical, the inefficiencies of traditional contract review can hinder the timely execution of contracts and impede the organization's ability to capitalize on emerging opportunities.

Here’s how Machine Learning can turn the tide.

#1 Maximizing time efficiency

ML algorithms can quickly analyze and extract relevant information from large volumes of contracts, reducing the time it takes for in-house counsel to review and approve agreements.

This speed is particularly crucial in fast-paced business environments where quick decision-making and contract execution can be a competitive advantage.

#2 Less room for errors

ML algorithms don't process and interpret data the same way humans do. They're designed to be precise and consistent. Thus, they're far less prone to errors. Unlike humans, machines do not suffer fatigue, lack of attention, or biases that can lead to oversight or misinterpretation of critical contract terms.

By automating the review process, ML systems enhance accuracy and help ensure that contracts comply with legal standards and internal policies.

Moreover, ML algorithms can continuously learn and adapt from the data they process, improving their accuracy over time. This learning capability allows for a more refined and reliable contract review process, minimizing the risk of overlooking important details or making costly mistakes.

#3 Large volumes, no problems

Whether it's a handful of agreements or a massive collection of contracts, machine learning algorithms can handle the workload with consistency and efficiency. This scalability ensures that organizations can adapt to changing business needs, manage increasing contract volumes, and maintain a high level of accuracy in the review process.

This is particularly beneficial during periods of high contract activity, such as mergers and acquisitions or rapid business expansions. Machine Learning allows organizations to manage and review a large number of contracts simultaneously, supporting agility and responsiveness in dynamic business environments.

#4 Enhanced risk management

By analyzing historical data and recognizing patterns indicative of risk, ML algorithms can provide in-depth insights into the potential pitfalls associated with specific contract terms.

These algorithms can identify and flag non-compliance issues, ambiguous language, or terms with potential negative implications for the organization.

This allows legal teams to be more proactive with risk management, ensuring that the contract ultimately meets the expectations of both parties.

Also read: How to Choose the Best AI Contract Review Software

A new world of possibilities

Machine Learning contract review has emerged as a game-changer for legal teams who have found themselves lagging in a fast-paced business ecosystem where they're required to produce more results in less time.

The combination of speed, accuracy, and scalability offered by ML-driven solutions positions legal teams to crush their day-to-day objectives while delivering on strategic, higher-value aspects of their roles.

If you're ready to break free from the status quo and experience a new world of possibilities in contract review, it's time to get familiar with VerifAI, our powerful yet easy-to-use AI contract review tool. Click here to learn more and request access.