Digitization in contract management has accelerated greatly in the last few years. Organizations have realized the importance of contracts and the need for introducing automation for efficiency and accuracy. Organizations have also realized the risks associated with inaccurate contract language, missed clauses, and omission of even the minutest of details in the contract documents.
To avoid such risks and improve contract authoring for fool proof compliance and accuracy, manual contract management processes are just not enough. Especially considering the large volumes of contracts global companies deal in on a monthly basis. In order to take quality, accuracy and analytics to the next level and enable contracts to stand the test of ever-growing compliance and regulations, AI and machine learning powered smart contract lifecycle management solutions are gaining popularity.
Natural Language Processing (NLP) backed by AI and deep learning, is one such powerful feature that is transforming contract language like never before. Let us dig deeper and explore how NLP is helping create robust accurate contract documents and processes and preparing contract management for the future:
1. Parsing third party and legacy contracts
Natural Language Processing using deep learning can parse External Party Paper and legacy contract documents to extract key meta data like expiry and renewal dates, payment terms, clauses and obligations, etc. It saves time and man hours of having to go through pages and pages of documents in order to find all the key data included in contracts. It also ensures all important terms are highlighted without any manual omissions or errors in capturing all key information.
2. Classifying the extracted terms into pre-defined categories
Once NLP identifies all key data, it then segregates it into pre-defined categories, which in this case are usually terms, clauses, and sections for ease of perusal. Semantic analysis along with natural language processing can also be used for meta-data tagging in case of updates to regulations. It can be used to find clusters of related words that are relevant to the issue and in close proximity, surfacing them, and then applying new tags to these clauses. This is an effective way to ensure meta-data tags are constantly updated thus keeping the contracts from going redundant and non-compliant.
3. Identifying errors and inconsistencies and suggesting solutions
NLP, by constantly learning from past data, can identify the differences in language as compared to the standard contract language. It can gauge the percentage difference between internal and external clauses and in cases where contract clauses deviate over and above the pre-decided percentage, these are directly routed to the legal teams for review and modifications. The review process and therefore the negotiation process moves much faster when negotiators can quickly be informed how close the current version of the contract is to internal standards.
NLP makes contract management smarter and a more sophisticated process. It automates and refines contract authoring and reviewing, making it more accurate, fully compliant, and error-free. It enables organizations to gain more from contracts and avoid penalties that arise from inefficient authoring and non-compliance. Moreover, by gathering insights from past data, NLP opens doors to predictive analysis and better insights from contract data which can contribute to informed decision making.
Zycus CLM offers unparalleled insights into your contract documents, quickly providing you with relevant information about contract performance and regulatory compliance via a single view customizable dashboard. To learn more about how Zycus can ensure compliance across all your agreements and help you follow the best practices for contract management contact us now.
