Contract Management System Will Be Able to Cut Short The Legal Team Working Hours
Contract management is a tedious work. It involves analyzing the contract and clauses, followed by managing the pile of documents in the filing. Fortunately, a new system has been invented to help legal team.
One of the machine learning system is deployed by JPMorgan Chase & Co., Bloomberg reported. The system is a machine learning with capability to parse financial deals from the contract, which requires thousands of working hours from the legal team.
Contract management is a huge responsibility for legal team and loan officers. The team must analyze the contract and clauses in the contract, then examine the financial deals within the contract. Furthermore, the contract must be maintained and managed for further needs such as extension or amendment.
JPMorgan’s contract management software is called COIN, which stands for Contract Intelligence is able to analyze and interpret the commercial loan agreement. The software will be going online in June and will reduce the thousands of working hours consumed by legal team and loan officers in just a matter of seconds.
Another software that deliver similar capabilities is called alt-legal, developed by a startup company Kira System, Above The Law reported. In an interview with the Chief Commercial Officer Steve Obenski, Kira System has the same method to analyze contracts and diligence.
However, the contract management system is still in the early stage as admitted by Obenski. The machine learning, which was developed with the capability to read and interpret the document still find it hard to analyze the big contract.
“Over the past ten years, many have made progress extracting some data from those contracts,” Obenski said. He added another challenge in the contract management system is the contract database, “Creating these databases still requires a lot of manual labor, and it is a struggle to do it affordably.”
Watch the explanation of principles and practices of contract management below: