Wildfire reinsurance insurtech Kettle raises $25m Series A

Kettle, the reinsurance focused deep-learning and artificial intelligence (AI) insurtech that currently focused on California wildfire risks, has raised a $25 million Series A funding round to help it expand its business and further develop its advanced technologies.

Kettle aims to become an intelligent source of risk for providers of reinsurance capital and is agnostic to the source of that capital, following an MGA model and beginning with California wildfire risk.

Kettle aims to enhance the understanding of catastrophe and climate risks with its advanced technological approach to modelling risks, then establish mechanisms and structures to transfer risks to both traditional and capital market sources of reinsurance capacity.

Kettle received a license as an Innovative Agent in Bermuda earlier this year.

The company has also made some key hires in 2021, including William Haddrell as its Chief Underwriting Officer, Kevin Copeland as Chief Financial Officer (CFO) and Amit Shah as its Chief Distribution Officer.

The $25 million Series A funding announced today was led by Acrew Capital, with participation from Homebrew, True Ventures, Anthemis, Valor, DCVC, and LowerCarbon Capital.

With the insurance industry having seen a 68 percent drop in return on equity due to a 3X increase in catastrophes causing more than $1 billion in damage over the past 15 years, according to NOAA, Kettle wants to use its technology to help improve understanding of catastrophic perils and make underwriting them more informed and predictable.

Founded by Andrew Engler and Nathaniel Manning, Kettle is structured as a reinsurance Managing General Agent that can underwrite these increasing risks.

Kettle is also in the process of setting up their own risk-bearing entity, although other sources of reinsurance capital are also likely to remain important to the insurtech.

“We are thrilled to be helping provide insight and relief to the California insurance market,” explained Manning.

“There are 14 million structures in California, and in 2020 ~11,500 of them burned down, less than .1%. While the risks of wildfire have certainly increased over the past decade, the key is understanding exactly where the risk is. If we can do that, we can bring stability back to the California insurance market.”

In 2020, Kettle’s model predicted that the fourteen largest fires, which accounted for 98% of the damage, were in the top 20 percent of areas most likely to burn across California’s hundred plus million acres.

In 2021, Kettle’s model has also predicted the areas consumed by the Dixie and Caldor Fires, highlighting them as some of the most dangerous parts of California.

Kettle’s technology and proprietary algorithms leverage terabytes of data from public and private data sources, such as NOAA weather data and NASA’s MODIS and LIDAR satellites.

The insurtech’s neural networks run upward of 140 million model parameters in order to calculate probabilities of fire damage at the half square mile resolution across the state.

“When you take a minute to think about it, it becomes very obvious why traditional reinsurers can’t accurately underwrite climate risk — their methodologies look to the past,” explained Lauren Kolodny, Partner at Acrew Capital. “And our climate is changing in ways that can’t be predicted on the basis of historical data. Kettle is solving a massive, global problem. And we’re so thrilled to deepen our partnership with this incredible team.”

“Climate change is here, it’s right there in the words – the climate has changed,” added Manning. “Now we need to build the best safety net we can to help people recover from these catastrophes.”

This funding will help Kettle to advance its offering and technology and we suspect in time the insurtech will target other climate-linked catastrophe perils, especially those that are causing challenges for insurance and reinsurance underwriters due to fluctuating frequency and severity.

Advanced technology can assist in making risk more understandable and underwritten portfolios more predictable, through advanced data analysis, machine learning and use of big data.