The Serious Fraud Office (SFO) had a problem. Its investigation into corruption at Rolls-Royce was inching towards a conclusion, but four years of digging had produced 30 million documents. These needed to be sorted into “privileged” and “non-privileged”, a legal requirement that involves paying junior barristers to do months of repetitive paperwork. “We needed a way that was faster,” says Ben Denison, chief technology officer at the SFO. So, in January 2016, he started working with RAVN.
Pronounced “Raven”, the London startup builds robots that sift and sort data, not only neatly presented material, but also unstructured documents. “Where someone has scanned 300 pages, it’s not uncommon to put one page in upside down,” says co-founder Peter Wallqvist. “We need to deal with that real world of messy datasets.”
The two teams started to feed material from the Rolls-Royce case into the AI. By July they had a viable system, 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. “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. “It’s hard to imagine a better outcome,” says Wallqvist.