Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions for the insurance industry, announced that the company has joined Guidewire’s Insurtech Vanguards program, a new initiative led by property and casualty (P&C) cloud platform provider Guidewire, to help insurers learn about the newest insurtechs and how to best leverage them.
“Guidewire is one of the most recognized platform providers in the insurance industry today and we are proud to be working with the company,” said Stan Smith, founder, and CEO, Gradient AI. “As a part of the Guidewire Insurtech Vanguards program, we look forward to helping insurers improve underwriting and claim processes with our AI-power insurance solutions.”
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Insurtech Vanguards is a community of select startups and technology providers that are bringing novel solutions to the P&C industry. As part of the program, Guidewire provides strategic guidance to and advocates for the participating insurtechs, while connecting them with Guidewire’s P&C customers.
“Gradient AI is an effective, innovative, and proven insurance solution providing insurers the intelligence needed to significantly improve their efficiency and profitability in claims and underwriting operations,” said Laura Drabik, chief evangelist, Guidewire. “We are thrilled to welcome Gradient AI as part of our program so our mutual customers can harness the powerful benefits of AI and its competitive advantages in pricing, underwriting, and claims management.”
Gradient AI’s underwriting and claims dataset combined with its machine learning capabilities enable insurers to improve underwriting and claims outcomes. On the underwriting side, Gradient AI improves pricing accuracy, reduces loss ratios, and helps insurers to turn quotes around more quickly. On the claims side, it enables claims managers to reduce the duration and cost of claims. Gradient’s AI solutions learn from an insurer’s own data and from the company’s high-resolution federated database with millions of observations and ten times the features of traditional assessment processes.
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