AI Case Study
Thomson Reuters expedites the remediation process by 30% for clients using a machine-learning contract analytics platform
Thomson Reuters has partnered with eBrevia's machine learning platform to help with remediation undertaken by its contracts legal team. The platform identifies essential data points needed in the contract which is then used to propose amendments.
Consultancy And Business Services
"eBrevia’s AI engine powers Thomson Reuters contract remediation services by identifying three critical data points based on the client’s remediation plan: clauses needing revision, the absence of clauses required by the regulation, and the components required to generate a contract amendment. Thomson Reuters then leverages this intelligence to generate amendments in Thomson Reuters Contract Express, offering clients a “seamless workflow” for securing approval and digital signature."
Thomson Reuters claims it has sped up the remediation process by 30%.
Legal And Compliance
"The commercial contracts team predominantly work with in-house legal teams and this sort of tech collaboration is becoming even more interesting in the run up to GDPR and Brexit, as organisations face repapering exercises that are unprecedented in scale. Remediation has historically been labour-intensive, requiring large teams and manual effort – all of which equal extraordinary cost for clients. The TR (Thomson Reuters) commercial contracts team – which perform services of remediation driven traditionally by M&A but more recently regulatory change and the likes of Brexit have come to the fore – has used a number of extraction tools for many years and was one of the pioneers in the area, which makes the selection of eBrevia more, not less significant... 'We put together a comprehensive test plan across a range of tools and eBrevia met our needs in terms of accuracy going through large volumes of contract and things like how it dealt with finding data in tables – a lot of the old technology struggles in finding that data.'”