Contract analysis is a labor-intensive process, but AI has the potential to streamline it. ML technology helps organizations review contracts, identify ambiguous information and clauses and minimize risks.
The key is combining natural language processing with machine learning. NLP techniques like entity recognition, part-of-speech tagging, and sentence parsing help ML algorithms understand the structure of text and identify keywords or legal terms.
1. Automated Text Analysis
One of the biggest challenges in contract interpretation is the sheer volume and complexity of contracts that must be reviewed. This is where AI comes in.
The use of machine learning to automate the process of analyzing contract data can save legal and business users a significant amount of time, money and effort. Ultimately, this can improve operational efficiency, reduce risk, and deliver more accurate and insightful business performance data.
However, utilizing ML to analyze contracts requires a large dataset that is labeled in advance. This data is required to train the ML algorithm to recognize specific patterns, terms and conditions. To be successful, ML algorithms must be trained on a large number of similar contracts and a variety of different industries and types of agreements. This is why using ML to perform contract analysis presents certain risks that must be understood and managed.
Contract AI is an innovative solution that can help solve these issues and improve contract processing, indexing, and retrieval. It can speed up contract assembly activity for sell-side agreements and accelerate buy-side contract review processes, and it delivers a more efficient and effective approach to interpreting and managing contract data.
Using contract AI is a great way to make the most of your legal team’s expertise, and it can help you achieve a higher level of quality in your contract review. With the ability to process a greater volume of contracts in a shorter amount of time, leveraging contract AI can allow your legal and business teams to focus more of their attention on developing and executing a comprehensive legal strategy for your organization.
The key to successfully implementing contract AI is to ensure that the system is trained on a wide range of documents and industries, and that the training data is properly validated to avoid any biases or errors. With the right combination of tools and methodologies, leveraging contract AI can be an extremely powerful and valuable tool for your legal and business teams.
If you are looking for a partner to assist with your next project, reach out to Legly. Our software provides an extra set of eyes and can catch the things that your legal and business team may miss.
2. Predictive Modeling
Predictive modeling is a machine learning tool that looks at current and historical data to predict likely outcomes based on patterns and trends. It’s one of the premier ways a business can see its path forward and make plans accordingly. It can be used to predict just about anything from TV ratings and a customer’s next purchase to credit risks and corporate earnings.
There are many different predictive modeling tools and techniques, including regression and neural networks. These differ by how they look at the data and what type of variables are included in a model. However, each has a common goal: to increase the probability of forecasting an unknown outcome.
The first step in deploying predictive models is to gather the data that will be used. This is often the longest and most time-consuming part of a project. The next step is preparing the data, which involves turning raw variables into meaningful ones that will fit better with the prediction method chosen. It’s also important to choose a business case for the project.
Once the data is prepared, it can be run through the predictive model to test its accuracy. The results are then used to produce a forecast or recommendation. This can then be used to inform decisions and take action.
This approach can save companies a significant amount of time and money. It can also improve the quality of a decision, as it eliminates bias and human error. In addition, it enables businesses to find insights that may not have been discovered by human analysis.
Some legal and professional services firms have already made the switch to predictive modeling. IT firm Atos, for example, partnered with software maker LEVERTON to use the tool in performing due diligence in real estate transactions. The software is said to be able to scan documents and automatically identify risk terms that can be reviewed by subject matter experts.
Increasingly, business users and subject matter experts are using AI to do predictive modelling, which is becoming more accessible thanks to no-code platforms that automate the process and make it easy for anyone to do their own data mining. This has created a need for transparency and explainability in AI, but it is important to note that this doesn’t necessarily mean the need for human intervention in every instance.
3. Natural Language Processing
Natural Language Processing is a fascinating and rapidly evolving area of artificial intelligence. It combines computational linguistics-rule-based modeling of human language-with statistical, machine learning, and deep learning models to enable computers to process text and spoken words as well as humans do.
The legal industry is just beginning to explore how NLP can help make contract review and interpretation faster, more accurate and more consistent. Using NLP, attorneys can frame their searches as they would to a colleague, for example, by asking “What are the key terms in this contract?” or “What are the time limits on non-competes in New Jersey?” The program then uses that information along with thousands of other relevant search results to find what the attorney is looking for.
NLP can also help to automatically categorize clauses and provisions, identifying potential issues. It can also be used to identify key legal concepts, such as jurisdiction or term meanings and provide a list of the most commonly found terms in contracts. It can even be used to automatically check for legal compliance and detect breaches.
One of the challenges for NLP in contracts is the complex structure and ambiguity of legal language. Consequently, NLP research in the legal sector often focuses on systems that “keep the human in the loop,” automating while maintaining a certain level of control by the end-user or lawyer. This type of system can be highly useful and improve productivity, but it requires careful thought and evaluation as it is still an emerging technology.
A good guiding principle for this type of innovation in the legal industry is to focus on systems that are explainable and interpretable. This can be achieved by building audit trails and design patterns that allow users to overwrite machine suggestions, offer feedback, and re-train the model. This will go a long way to ensuring that these types of AI systems are trusted and understood by legal professionals, increasing adoption and support for this type of technology.
In a world that is increasingly dependent on automation, NLP is an important tool for legal technology innovation. NLP can help to automate repetitive, time-consuming tasks and free up resources for higher value work. This will help companies to be more competitive, save costs, and drive growth in the business marketplace.
4. Machine Learning
Machine learning is an umbrella term for algorithms that can solve problems that would be cost-prohibitive to develop by human programmers. Rather than being told what to do by humans, machine learning systems learn on their own by studying data and “finding” the best algorithms to meet their goals. This allows AI to automate tasks and streamline processes for businesses. It also increases accuracy and speed.
Unlike a human contract review, which requires a lot of reading and interpretation of legal language, AI can quickly process documents, recognize the most important terms, and highlight glaring issues in contracts. It can also help legal and business teams to manage risk by detecting potential risks that might result in litigation or a breach of contract.
Contract AI uses automated contract analysis to reduce the number of back-and-forth negotiations and to improve the efficiency of contract management by reducing time spent on routine tasks. It can identify key terms and clauses and automatically generate proposals for changes to contractual agreements. It can even identify and flag ambiguous information or errors.
A growing number of companies are deploying contract AI solutions to streamline the contract review and management process, enabling them to increase revenue and reduce risk. It is also helping to improve transparency and accountability in the contracts, enhancing the speed and quality of decision-making, and improving compliance with laws and regulations.
However, despite the many benefits of contract AI, it is important to remember that neither human review nor artificial intelligence are perfect. It is critical to have a robust process in place to ensure that all the necessary information is captured and reviewed, and that any redlines are addressed appropriately. It is also important to understand how an AI system performs its work, and that it is not producing different outputs based on its own biases or assumptions.
One example of this is the way that a rules-based system of contract analysis will teach itself over time to recognize specific clauses, like nondisclosure. It will then be able to route those contracts to the correct in-house team without the need for manual intervention.