AI Case Study
Serious Fraud Office reduced lawyer workload by 80% by automating document classification through RAVN's AI platform
The UK's Serious Fraud Office used RAVN's AI system for document analysis and classification to determine which were privileged or not and therefore obligated to be shared or not with opposing counsel. The system reduced 80% of the legal team's manual workload.
Public And Social Sector
"The two teams started to feed material from the Rolls-Royce case into the AI. By July they had trained an algorithm, and with the agreement of lawyers on both sides, they set the robot to work. The barristers were wading through 3,000 documents a day. RAVN processed 600,000 daily, at a cost of £50,000 - with fewer errors than the lawyers."
Legal And Compliance
"The Serious Fraud Office had a problem. Its investigation into corruption at Rolls-Royce was inching towards a conclusion, but four years of digging had produced a massive pile of documents: over 30 million, including everything from spreadsheets to emails about staff away days. Now these needed to be sorted into “privileged” and “non-privileged”, a process that meant paying junior barristers for months of dull, repetitive paperwork. 'We needed a way that was faster,' says Ben Denison, chief technology officer at the Serious Fraud Office. So, in January 2016, the SFO started working with RAVN."
"'It cut out 80 per cent of the work," says Denison. 'It also saved us a lot of money.' For Rolls-Royce, it had the opposite effect. In January 2017, the engineering company admitted to 'vast, endemic' bribery and paid a £671 million fine."
RAVN's systems "mixes applied AI techniques such as computer vision with more conventional database management, can digest not only neatly presented material, but also information that comes in a less structured format. 'We have things like an upside down detector,' says co-founder Peter Wallqvist. 'Where someone has scanned a 300-page document, it's not uncommon to put one in upside down by mistake. We need to deal with that real world of messy datasets.'
Over 30 million documents, including spreadsheets and emails