The purpose of our business is to drive-down Total Cost of Risk. We’re doing this by pioneering a new asset-class, and leading the securitisation of reinsurable risk on a massive scale.


The risk-transfer side of our business uses #DSVP—our proprietary supervised learning algorithm—to do two things:

(1) help clients to improve their pricing of re/insurable risk;

(2) generate risk-adjusted returns for providers of capital.

#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.