Hundreds of processes, thousands of people and infinite risks – This is how we define business. A complex landscape, which apart from employing talented and efficient people to carry out its functioning, also engages with several vendors and suppliers across the globe, adhering to a myriad of frequently changing local laws and compliance demands.
It is this complexity that exposes an enterprise’s operational teams such as procurement, vendor management and even human resources (HR)—to a wide range of risks throughout the contract authoring process, and long after. Why long after? Because, contracting is not a one-time exercise but a continuous process. One cannot simply create a contract, get the parties to sign and keep it aside. Contracts are like written constitution for conducting business between parties. One single issue of non-compliance can lead to inefficiency and cause huge losses. To avoid this, there are various questions to be identified in a contract and answered time to time.
Most enterprises fail to answer these questions due to various reasons. Most often, the operational teams aren’t familiar with the exact terms and conditions of the contract, which leaves the contracts ambiguous, forcing the enterprises to collate all historical documentation and link related information only in the aftermath of a trigger, say a breach or when risks manifest.
Though contract risks pose a major danger, they are often overlooked by companies. Not only do companies lose out on revenue, they also become inefficient and put themselves in danger of noncompliance with a client or partner, risking their reputation and future business opportunities. Research findings say that inefficient contracts lead to losses between 5% to 40% for enterprises across industries. This is cited as one of the reasons why enterprises have started leveraging artificial intelligence to overcome such challenges of contracting. But before we explore AI for contracts analysis, let’s understand the three types of contract risk.
First one is Maverick contracting, wherein the contracts are executed that violate company controls to ensure regulatory or commercial compliance. Second risk is when the buyer or supplier doesn’t meet contract’s terms, thereby increasing the possibility of losses for the organization. Third is when a contract doesn’t perform as per expectations, causing losses for the organization.
To avoid possibility of such risks leading to financial losses, non-compliance and unfavorable lawsuits, it is imperative to have key procedures in place to identify and monitor contract risk. Effective contract risk management reduces risks and gives enterprises better control over their contracts. This enables the companies to control what contract language is used across the enterprise, recognize underperforming contracts and take informed actions, when a contract’s terms are not being met.
This can be done by leveraging AI and machine learning, which help proactively identify risk from contracts and send out alerts for overlooked risks.
As we often say, “god is in the details”. When enterprises have clear, specific and on-demand visibility into their contracts, it becomes easy to mitigate risks. A contract analysis solution can give you exactly this by breaking down contractual provisions (aka Intents) and surgical abstraction (aka Entities), enabling ease of reading and interpretation.
Manually performing these tasks would require weeks or even months of human efforts and basic automation might not provide accurate results. ML powered technologies help automate this process and give accurate and quick results. Empowered with advanced capabilities, leading solutions provide improved contract oversight, proactive risk identification, and continuous risk prevention.
A cognitive contract analysis can help identify operational risks by:
Ease of reading and interpreting the documents can mitigate revenue leakage and ensure accurate and timely recognition. With the advent of artificial intelligence technologies, machine learning, natural language processing and cognitive automation, enterprises are able to analyze their contracts and have real-time visibility of their risks at a fraction of the price and effort. By setting the right expectations and taking adequate steps, technology can assist you in managing large volumes of contracts efficiently and in a cost-effective manner.
Nia Contracts Analysis utilizes advanced Machine Learning (ML) techniques to automate contracts extraction, risk analysis and review of unstructured contracts. It acts as a single source of truth to answer any kind of contracts-related information.
Nia Contracts Analysis leverages ML techniques such as vision-based, semantics-based, and language-sequence-based to transform the process of analyzing and reviewing contracts. It provides an intuitive workbench with different personas to configure and train ML models.