Lemonade Chief Pushes Back Against Insurance Firm’s Doomsayers

Digital insurance firm Lemonade says predictions of its demise have been greatly exaggerated.

The company’s stock has fallen more than 70% since it went public in 2020, and saw its shares fall again last week.

But CEO and Co-Founder Daniel Schreiber told the Financial Times (FT) Sunday (March 3) that the company’s results, which showed a small increase in cash and investments, help disprove the naysayers who predicted Lemonade was “going to burn through [its cash] and go out of business.”

“We are not burning through it, our cash levels are going up, so I think a lot of the questions about survivability . . . ought to have been put to bed,” he added. The company expected net cash flow to turn “consistently positive” in the first half of next year.

The company, which offers home, car and pet insurance, has also seen its use of generative artificial intelligence (AI) grow as well, Schreiber added, with about 20% of all emails Lemonade receives — a harder area to automate than in-app communications — responded to by generative AI.

“We don’t see an upper limit to that,” he said, adding that generative AI was able to provide responses that were “entirely empathetic and precisely worded.”

And as PYMNTS wrote last week, the company has also seen AI improve its loss ratio, or the ratio of losses to premiums earned. That figure improved by 12 points year over year, Lemonade said in this letter to shareholders.

“Our improving loss ratio, and our growing powers of prognostication, owe much to our suite of homegrown AIs, and specifically to one we refer to fondly as ‘LTV,’” Lemonade said in the letter. “This composite AI harnesses some 50 machine learning models to predict, for each customer, their lifetime value, or LTV, in net present dollar terms.”

PYMNTS also looked at the role of AI in the insurance sector in an interview with Foxquilt CEO Mark Morissette, whose company employs data analytics and AI to suggest the best insurance coverage and price through brokers, agents and enterprise partners.

“You need to define that customer and then go and solve their problems,” he said. “But at the same time, you need to generate a sustainable business model that generates underwriting profitability and build a smarter machine that facilitates solving customer problems.”