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Verisk Analytics (VRSK) Q2 2026 Earnings Call Transcript

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Artificial IntelligenceCompany FundamentalsCorporate EarningsCorporate Guidance & OutlookCredit & Bond MarketsMarket Technicals & Flows

Verisk reported Q2 2026 revenue of $806M (+4.0% YoY; +5.8% organic constant currency) and adjusted EBITDA of $464M (+7.4% on an OCC basis), with diluted adjusted EPS rising 5.3% to $1.98. The company reaffirmed FY2026 guidance for revenue of $3.19B–$3.24B and adjusted EBITDA of $1.79B–$1.83B (56%–56.5% margins), despite caution on transactional revenues from softer commercial property volumes and below-average hurricane activity; AI-related products (XactAI user growth to nearly 10x since March and 7,000 licenses) were cited as supporting improved price realization and subscription momentum. Capital return remained aggressive with $1.9B of share repurchases in the first six months of 2026, retiring ~8.5M shares and lowering the weighted average share count by 6.8%; net cash from operating activities rose 50% to $366M and free cash flow rose 58% to $298M.

Analysis

This is less a “beat” than a proof that VRSK is converting itself from a cyclical data vendor into a higher-duration workflow platform. The key mechanism is that AI is not being monetized as a standalone product; it is showing up first in renewal uplift, seat expansion and stickier usage inside underwriting/claims workflows, which should support multiple durability even if headline growth stays mid-single digits.

The near-term overhang is mix: transactional revenue remains the swing factor, and soft commercial property plus subdued event activity can mask the underlying quality of the subscription book for a quarter or two. That said, because transactional is the smaller piece and the company is still buying back stock aggressively, EPS can compound faster than organic revenue, which should limit downside unless the market starts questioning renewal pricing or the pace of AI adoption.

The contrarian read is that consensus may be overfocusing on the AI narrative as incremental TAM, when the more important effect is moat reinforcement. If carriers build their own models, they still need governed datasets, semantic context and workflow integration, which favors incumbents with embedded data rights over generic AI tooling. The thesis breaks if renewal growth slips or if Q3/Q4 commentary shows AI usage is still experimental rather than monetizable.

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