Clifford Chance
When given a short turnaround time, each employment litigation lawyer on the team here at Clifford Chance is well-versed in working under pressure to get the job done.
However, there are tools that can be introduced to help speed up processes in order to deliver a high-quality service on time and to our clients’ exacting standards. This was the case when a global strategic client of ours instructed our Paris litigation team to work on a matter that involved reviewing a set of employment contracts. Our lawyers were tasked with identifying relevant information and providing advice in relation to their findings.
As this was a huge undertaking, machine-learning software was used. This reduced our employment litigation lawyers’ efforts by over 50% and sped up delivery to the client.
Here is a look at how this came about and what processes were involved.
CASE STUDY | CONTINUOUS IMPROVEMENT AND TECHNOLOGY
The challenge
What happened when the employment litigation team had to review over 1,000 contracts?
The employment litigation lawyers were faced with an extremely short deadline to review over 1,000 employment contracts. Most of the contracts were written in French, with others in German.
Traditionally, a lawyer would coordinate the review exercise with colleagues via email. Contracts would be sent to Clifford Chance lawyers in the relevant countries. The lawyers would then review all 1,000 contracts and send back their findings to be collated into a report for the client.
The solution
How was machine learning introduced to this task?
Due to the tight deadline, the employment litigation lawyers worked with our Innovation team to work out how to speed up delivery. The team provides a dedicated service that brings legal disciplines and technology together to create a smooth, efficient offering. In this case, the team was able to use this combination to troubleshoot the task at hand.
To begin, the team ran a short workshop to understand the process and the output required. The result of this workshop was the decision to introduce some machine-learning tech to the task.
The team, working with the employment litigation lawyers and their secretaries, created a list of key words and phrases that would indicate that a contract needed further scrutiny. From there, all 1,000 contracts were uploaded into a machine learning platform and searched for the key terms.
The result
What happened after machine learning was introduced to this employment litigation task?
The introduction of our machine-learning capabilities meant that the process was sped up and the deadline was met.
Using the technology we were able to immediately identify 550 contracts that could be excluded from further review as they did not contain any of the identified words or phrases.
This meant that each employment litigation lawyer in the team could concentrate their detailed manual review on the remaining 450 contracts. The online platform allowed us to assign the contracts to the relevant French and German-speaking lawyers quickly and efficiently. The manual review of the reduced sample was then completed online.
Our team estimate that the lawyers were able to deliver the final report to the client one week earlier than would have been possible using traditional methods. Therefore, bringing this machine-learning method to the task of reviewing the contracts was a success.
