The purpose of our business is to lead profound sustainable change in global re/insurance markets and drive down the Total Cost of Risk.

The risk-transfer side of our business uses ‘DSVP’, a proprietary supervised learning algorithm, to enable us to do two things: (1) help clients to price re/insurable risk; and (2) generate risk-adjusted returns for providers of capital.

Some context for this. ‘DSVP’ is the synthesis of five traditional re/insurance pricing methodologies, developed over the last 300 years, and now coded into ‘R’, the statistical programming language. Basing ‘DSVP’ in ‘R’ helps us to overlay risk-data for all kinds of types of insurance and reinsurance, with linear regression models so that our team can quickly understand, then structure and price re/insurance, as we focus on generating underwriting profits and risk-adjusted returns.

The next step for us will see us invest in the development of ‘DSVP’ so that we can automate the re/insurance-pricing process; and so that we can use unsupervised learning algorithms to explore patterns in risk-data and evaluate significance of features that have been traditionally considered significant. We are currently fundraising for this.