WHAT WE DO
Our clients process lots of legal claims, insurance claims and customer complaints. But when a new claim arrives, it’s hard to know whether to settle or contest, and how much to spend in the process.
At the moment, a person has to read through the papers and come to a view. Slow or wrong decisions increase the risk of losing customers and getting bad publicity. So UK companies spend 16 millions hours doing this every year.
Sibyl instantly predicts claim outcomes using cutting edge artificial intelligence. Clients can settle claims that are likely to succeed or low in value, and let their best people concentrate on high-value marginal cases. This improves speed of service, provides extra quality assurance and eases operational pressure.
HOW SIBYL WORKS
Sibyl can read from a range of file types at low resolution, including word, pdf and jpeg. Simply drag and drop the initial claim documents, complete some basic fields and press go to get your prediction.
Use Sibyl's predictions to make quick and cost-effective decisions about which claims to contest, which claims to settle and how much to pay.
Set claim handling strategies, track claim handling costs over time and optimise performance by reviewing new strategies against historic claims.
The team met at the Online Courts Hackathon organised by HM Courts and Tribunals, Legal Geek and the Society for Computers and Law. There were 200 participants, including teams from top law firms and tech companies. But we won the award for Coolest Tech.
Sibyl was born from this effort to make the justice system work better. As more organisations use our technology, people with good claims will get a remedy faster, avoiding long and stressful handling processes or protracted litigation. On the other hand, people with bad claims will find it harder to sneak under the radar, as even low value claims will face challenge where warranted. Everyone stands to benefit from these efficiencies.
THE SIBYL TEAM
Head of Commercial
Richard is qualified lawyer with 5 years post qualified experience across the City, in-house and Government.
Head of Product
Nik is a doctoral candidate in engineering at Oxford, with experience in the investment research industry.
Head of Technology
Sivo is a doctoral candidate in machine learning with commercial development experience at EuroRisk Systems.
Sneji has a Master’s in computer science and commercial experience developing a predictive portfolio management system.
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