Utilizing Machine Learning to Enhance Legal Contract Review Efficiency

I believe this was done in 2019/2020. To my surprise, the innovation acceleration funnel had many AI based applications after the blockchain period -- this demonstrates how well the funnel is keeping up with the world's interests. I consider this to be a good sign, but it remains to be seen how many of these prospects can be converted into real results. This experiment seeks to determine if the use of a machine learning algorithm with a combination of supervised and unsupervised learning is capable of enhancing both the effectiveness and efficiency of legal contract evaluation. Many firms of significant size have to manage a large number of legal contracts, to purchase products and services for operations taking place currently, as well as for future investments. It is the task of the in-house legal team to make sure that the company's rights and duties are adequately set forth in legal agreements. The in-house legal team is responsible for finding the right compromise between two aims when undertaking this duty. two tiers. In order to reduce the review workload for legal contracts, the FI has opted for a risk-based approach when analyzing them. Basically, legal contracts are divided into two groups. In spite of taking these steps, the internal legal squad still devotes a substantial amount of time to overseeing both sorts of legal arrangements. In order to address the issues stemming from legal contract review, the FI and a FinTech partner joined forces to experiment with the potential of a machine learning algorithm for the process. tasks It is envisioned that the machine learning algorithm can be trained to perform the following tasks They planned to use supervised learning to instruct the machine learning algorithm, rather than unsupervised learning. Supervised learning is a kind of machine learning approach where an algorithm is trained to do a job using labelled data. Labelled data is information which is already marked with the "right" answer, and the algorithm can use that to identify other examples of the "right" answer if given a comparable data set. Unsupervised learning is an alternate type of machine learning technique, where an algorithm is directed to find out to execute a task with no labelled data. The expectation is for the algorithm to independently decide the "correct" outcome. In order to equip the machine learning algorithm with the ability to produce dependable suggestions for legal inquiries, it is necessary to provide regular, purposeful feedback rather than allowing the algorithm to establish its own conclusions. If successful, the experiment has the potential to substantially simplify the workings of the FI's in-house legal team. The improved efficiency and productivity could enable the in-house legal team to provide more prompt and thoughtful legal services to the business units.

Source: https://medium.com/@notherArmchairEconomist/experiment-to-improve-the-workflow-of-legal-contract-review-with-machine-learning-c745cd8462c6?source=rss------fintech-5