Hopefully, from the last post, it is apparent that there is a disconnect that exists in the commercial property underwriting world. There was certainly a lot of feedback from the first post over on Linkedin. For example, I loved this in depth response:
This blog post has been five years in the making. Five years ago, we started exploring how our company could help with policy wordings. Immediately, we started hearing from commercial property underwriters. Can you help us review broker-manuscripted policies, these underwriters asked.
I have been reading with envy as business to consumer (B2C) insurtechs have been posting their quarterly updates. We are a simple business to business (B2B) insurtech company. But then I got to thinking -- why can't we do the same? Keep reading for some updates and the best insurance innovation advice I heard in 2018 (so far).
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.
The following series of posts survey how the seemingly nascent features of an insurance policy - policy metadata - can be levereaged to create artificial intelligence that improve the quality and efficiency of drafting these policies. By using even the most rudementary AI tools, underwriters and legal professionals can begin to speed up the work they are doing by more effectively spotting areas that deviate from industry standards.
This week I attended the World Economic Forum workshop on Artificial Intelligence and Financial Services in New York City. It was very surreal to be representing the insurtech world with
Fractal Industries
at the event.
I have two big takeaways after listening to people much smarter than me prognosticate on the future.
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).
Something interesting is going on in the insurtech space that I thought I would point out.
It feels like insurtech is maturing.
I am on a plane to California typing this out. It's my second week in a row visiting San Francisco. I am not attending a conference. Instead, I will be presenting to an Insurance Company's Board and Executive Leadership for the second week in a row.