Using Applied ML to Solve Central Problems in the Insurance Industry

Using Applied ML to Solve Foundational Problems in the Insurance Industry

In AI communities, there is a famous language semantic test to see whether an algorithm can resolve named entities from the following tortured sentence: Paris Hilton (person) was spotted in Paris Hilton (city, state) at the Paris Hilton (hotel) listening to Paris Hilton (song). Today’s commercial property insurance lingua franca, Statement of Values, often reads to risk managers and underwriters like the above sentence, only without punctuation. No wonder frustrations are growing across the value chain.  

AI is part of the solution to this semantic mess, but as I’ve learned through the course of investing in multiple analytics, AI and fin/insurtech start-ups, tech alone isn’t sufficient to change a business ecosystem deeply invested in existing behaviors and current patterns of work. What I find compelling about Archipelago is taking an intensely pragmatic approach to resolving simple yet real pain points.

What if risk managers, brokers and underwriting teams could remove a ton of grunt work updating, analyzing and sharing SOVs? What if today’s costly insurance roadshow became a virtual, highly efficient placement process? What if underwriting teams dramatically improved their capacity to handle more inbound opportunities without additional staffing? Archipelago’s applied ML takes the grunt work, headaches and wasted time out of insurance renewal. And with Covid-19 and work from home, Archipelago helps the entire commercial real estate industry quickly adapt to a distributed, more profitable way to underwrite and manage asset risks.