Legal data is incredibly valuable. In the insurance field, data about the average similarity score of the clauses in a policy - a measure of the relative similarity or divergence for one clause in relation to a set of other similar clauses that are meant to accomplish the same function - can be used to instantly show how one policy stacks up against another. Data about the composition of cyber policies can be used to reveal which clauses or information a given policy might be missing.
I. Reviewing documents
The way documents are reviewed is broken. Having been through law school and worked as a doc reviewer in a law firm, I thought I knew what document analysis entailed. It was not until I expanded my perception of what document review could be that I realized how wrong that thought was.
In March, I started working for RiskGenius. As a company, we store files for different groups within the insurance community and provide layers of analytics on top of that to improve the operations of these groups. The type of analytics we provide range from a red line feature, which allows people to compare language from two policies against each other, to a clause score, which leverages multiple types of machine learning to gain an understanding of how similar one clause is to other clauses within a specific line of business.
Hi, I've missed you. RiskGenius has missed you. It's good to be back.
We didn't really go anywhere. The RiskGenius team just put it's head down and started doing the work. We worked on large deployments. We figured out how to scale supervised review. I went on a self-imposed insurtech conference hiatus. And we introduced our own proprietary machine learning platform (more on that below).
What would you do if you could go to Google.com and search for insurance clauses and policies?