Soteris, a Y Combinator-backed machine learning company, has launched a new AI-powered tool designed to help property and casualty insurers and managing general agents identify policies that are draining profits. The tool leverages machine learning to analyze policy characteristics and deliver actionable profitability insights at every stage of the insurance lifecycle — without requiring changes to rates, policy forms, regulatory filings, or staffing levels.
Soteris Targets Hidden Policy Losses
The launch comes on the heels of a strong year for the U.S. property and casualty insurance sector. According to AM Best’s latest financial review, the industry recorded a net underwriting gain of US$60.9 billion in 2025 — nearly triple the US$22.1 billion reported in 2024. The combined ratio improved by 3.7 points to 92.9. Separately, Verisk and the American Property Casualty Insurance Association estimated the underwriting gain at roughly US$63 billion, with the combined ratio improving to 92.9% from 96.6% in 2024. Fitch Ratings called this the industry’s strongest performance in two decades, with the combined ratio reaching its best level since 2006.
Soteris’ original loss-ratio product has been used by carriers and MGAs since 2020, scoring more than 100 million policy submissions covering over US$180 billion in premiums.
Moving Beyond Loss-Ratio Analysis
Soteris’ new product goes beyond traditional loss-ratio analysis by bridging the gap between predicting a policy’s loss ratio and determining its actual profitability. The economics of a single policy can be divided among multiple entities — including companies handling sales and servicing, state licensing, and capital backing.
“Every insurer knows they’re writing policies that will lose them money. They can’t find those policies with the resources currently at their disposal,” said Sunit Shah, founder and CEO of Soteris. He described this as the “blind spot” the company aims to address.
Millions of Policy Combinations
Insurers commonly rely on spreadsheets and pivot tables to assess policy performance. Soteris’ approach generates millions to billions of segment combinations by analyzing multiple policy characteristics simultaneously and treating each policy as its own credible segment. Implementation takes under 90 days. Once active, the system delivers insights in under 250 milliseconds through an API at any stage of a policy’s lifecycle, including quote or binding.
Users of Soteris’ original loss-ratio product reported improvements of five to 15 percentage points within a year. In proof-of-concept work for the new product, participating insurers saw book EBITDA increases of 70% to 125%.
Funding and Company Background
Soteris has raised more than US$8 million in seed funding from Spider Capital, Khosla Ventures, Intact Ventures, Amplify Partners, Foundation Capital, and Webb Investment Network. The company was part of Y Combinator’s 2019 cohort. Founder Sunit Shah previously built pricing models for life insurance and spent two years building a US$750 million P&C insurer within hedge fund Pine River Capital.
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