(Reuters) – Verisk Analytics reported a better-than-expected profit for the fourth quarter on Wednesday, driven by strong demand for its data analytics products used by insurers to assess policy risks.
WHY IT’S IMPORTANT
Property and casualty (P&C) insurers have been facing higher losses due to claims triggered by increasing numbers of extreme weather events around the world, prompting them to use analytics to help evaluate underwriting risks.
In January, AccuWeather forecasted total economic losses from the Los Angeles wildfires to range between $135 billion and $150 billion.
Meanwhile, Verisk said insured property losses from the Palisades and Eaton fires are in the range of $28 billion to $35 billion, one of the highest loss estimates from the industry.
CONTEXT
New Jersey-based Verisk primarily serves the P&C insurers, providing catastrophe modeling and predictive analysis to help them assess risk and optimize policy pricing.
In recent years, the company has leveraged AI to deliver deeper insights into emerging risks, improving underwriting and claims management.
Verisk’s shift towards subscription-based services has driven stable recurring revenue.
BY THE NUMBERS
Verisk’s consolidated fourth-quarter revenue rose 8.6% to $736 million from a year earlier, beating analysts’ average estimate of $733.7 million, according to data compiled by LSEG.
The company earned $1.61 per share on an adjusted basis in the three months ended December 31, up from $1.40 a year ago.
Analysts, on average, were expecting earnings of $1.60 per share.
Underwriting revenue increased 6.8% to $512 million in the reported quarter, while claims revenue climbed 13% as demand for the company’s anti-fraud and property estimating solutions grew.
However, the company forecast 2025 adjusted earnings per share in the range of $6.80 to $7.10, below analysts’ estimate of $7.31 per share, citing the impact of the divestiture of its Atmospheric and Environmental Research business, among other factors.
(Reporting by Prakhar Srivastava in Bengaluru; Editing by Shailesh Kuber)
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