Soteris delivers AI-based pricing software to the insurance industry. We are working hard to revolutionize the entire insurance value-chain and would love to stay in touch with updates.
Our first learning system understands the complex combinations of factors that drive your insurance risk - at a level of granularity beyond which humans can see - then uses what it has learned from your existing submission and loss data to score both current policies and future submissions to help you triage and price them.
In our pilot with our first customer, we fed our system a set of actual insurance policy and claim data to learn off of, then asked it to 'score' a separate set of policies it had never seen before. Each policy's score represents the system's estimate of the loss costs (incorporating both frequency and severity) of that previously-unseen policy. To test its predictive accuracy, we then sorted those policies top-to-bottom based solely on the assigned scores and split those policies into ten equal-sized buckets, best scores to worst, and then calculated the actual, eventual loss ratio for each bucket. The graph below displays the result -- the Soteris scoring system demonstrates a clear ability to predict which policies are likely to end up as loss-leaders, and which ones are not.
Dr. Shah entered the insurance industry in 2005, spending six years building pricing models for life insurance. He subsequently joined the $16 billion hedge fund firm Pine River Capital Management in 2013, where he priced risk within financial markets as the second-in-command on the macro and commodities trading team. While there, he spent two years building a $750 million property & casualty reinsurance company from the ground up. He is an author of Behavioral Finance and the Principal-Agent Problem in Finance and believes that behavioral factors should enter into any good insurance analysis. Prior experience includes the Boston Consulting Group and adjunct faculty positions at the University of Virginia. He received his Ph.D. in Economics from the University of Virginia, focusing in Econometrics, Finance, and Game Theory.
Dr. Mehta's expertise is in creating algorithms to solve challenging problems. This experience began in 2005 at the $30 billion hedge fund firm Citadel, where he eventually served as Head of Quantitative Strategies for Citadel Europe. He has spent over a decade writing algorithms which use a variety of mathematical and machine learning techniques to forecast future price movements, respond to competitors, identify inconsistencies, and trade across a variety of asset classes globally in financial markets and has also developed complex models and algorithms for other industries and mobile apps. He has a Ph.D. in mathematics from the University of Chicago and completed his postdoctoral fellowship at the Max-Planck Institute in Germany.
Mr. Phoenix is the co-founder and CEO of Vicarious, which does fundamental scientific research into the nature of intelligence with the goal of building machines that exceed human intelligence. He is an advocate for the development of safe AI and a leading signatory on the Future of Life Institute's Open Letter on Artificial Intelligence. Before co-founding Vicarious, Mr. Phoenix was CEO of Frogmetrics (Y-Combinator S2008), Entrepreneur in Residence at Founders Fund, and CXO at OnlySecure and MarchingOrder. Mr. Phoenix earned his BAS in Computer Science and Entrepreneurship from the University of Pennsylvania.
William Jewett has over 30 years of experience in the insurance and reinsurance industry. Until May of 2012, Mr. Jewett was President and Board Member of Endurance Specialty (NYSE: ENH), a Bermuda-based insurance and reinsurance company. Prior experience includes Converium Reinsurance, Centre Re, NAC Re, and Prudential Re. He earned his BA in History and Science at Harvard University and his MBA from The Wharton School at the University of Pennsylvania.
Whether you are interested in seeing a demo of our technology, partnering for a proof-of-concept, or just learning more about Soteris, we would love to talk.