MTech Capital co-founder and partner Kevin McLoughlin was featured in the latest Willis Towers Watson quarterly insurtech. See below for an excerpt of the interview:
"We have made several investments in embedded insurance, which we see as the opportunity to deliver insurance to a customer while they are doing something else.
There are two examples of this in our portfolio:
1) DealerPolicy is a digital agency offering the ability to buy auto and home insurance when a customer is purchasing a new vehicle from a dealer. The company integrates into the car financing workflow to collect key information on the vehicle and the applicant and uses this to seamlessly generate a comparison-shopping experience for their new car.
2) Matic offers a comparable proposition within mortgage servicing and origination channels. When a consumer calls in or goes online to pay their mortgage, Matic’s technology can often offer potential savings on their home insurance without a consumer even requesting a quote. Matic uses both the property and insurance details held by the mortgage company to generate bindable quotes from the best consumer brands in insurance. The embedded insurance model is attractive because it leverages data in one transaction to provide a better experience for the customer at a lower acquisition cost for the insurer.
Most people don’t want to spend time thinking about insurance. The deep ecosystem integrations achieved by companies like DealerPolicy and Matic make the insurance purchase fast and simple for the consumer while saving them money. And at the same time, delivering highly attractive unit economics — especially when the alternative might be pouring money into Facebook ads or Google AdWords.
We're excited about the potential application of the embedded model both for other lines of insurance and in other ecosystems where consumers and small business owners are doing something else and insurance is offered as part of that process."
The embedded model works particularly well in more commodity lines of business where there are high levels of automated underwriting and the ability to quote and bind digitally. We also look for a data advantage: If a consumer is buying a product, we want to take all of the information they’ve already provided during the purchase flow and seamlessly offer a bindable insurance quote using this data.