eBrevia and Big Four firm PwC announce a joint business relationship to apply AI driven review across a wide range of applicable matters including GDPR, Brexit, IFRS 16 and ASC 842, IFRS 15 and ASC 606, mergers, acquisitions, divestitures, financings, and real estate portfolio review. PwC's use of eBrevia industry leading AI software will increase efficiency and accuracy of abstraction of key terms from unstructured data. This relationship is a result of over 2 years of evaluation and use of eBrevia's software. PwC has been ranked as the most prestigious accounting firm in the world for seven consecutive years by Vault Accounting 50.
“Integrating eBrevia with our solutions has been instrumental in our drive to innovate and deliver greater value to PwC’s clients. Our solutions were digitally-enabled and significantly benefited from the software’s machine learning capabilities, its ease of use, accuracy and speed,” said Alexandre Blanc, a PwC Principal. “This has differentiated us in the market, and we look forward to further incorporating eBrevia into PwC’s existing solutions and innovation strategies.”
Using eBrevia to meet tight deadlines, small teams can quickly review large quantities of documents in connection with GDPR, Brexit, lease accounting standards IFRS 16 and ASC 842, and revenue recognition standards IFRS 15 and ASC 606, as well as mergers, acquisitions, divestitures, financings, and real estate portfolio review.
“We are thrilled that clients have benefited from the combination of PwC’s deep domain expertise and eBrevia’s cutting-edge machine learning technology,” said Adam Nguyen, eBrevia’s Co-Founder. “With eBrevia’s Bespoke self-training module, PwC has leveraged its extensive knowledge to train our system to extract granular custom data for many types of transactions and industries. Coupled with eBrevia’s out-of-the-box, pre-trained provisions and an intuitive user-interface, our collaboration has delivered significant gains in efficiency for clients.”
PwC Collaborates with eBrevia to Deploy Machine Learning for Contract Analysis