Verisk Analytics (VRSK) Operating Expenses (2010 - 2026)
Verisk Analytics' Operating Expenses was $442.6 million in Q2 2026, up 5.8% from $418.3 million a year earlier and up 2.8% from the prior quarter.
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Analysis
Verisk Analytics (VRSK) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Verisk Analytics' Operating Expenses was $1.76 billion through Jun 30, 2026, up 5.2% year-over-year; for FY2025, it came in at $1.73 billion, up 6.2% from FY2024.
- Operating Expenses has now increased for three consecutive years, with a five-year compound annual growth rate of 5.7% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $1.63 billion in FY2024 (+5.0%), $1.55 billion in FY2023 (+42.1%), $1.09 billion in FY2022 (-29.7%) and $1.55 billion in FY2021 (+18.1%).
- Quarterly Operating Expenses has moved between $20.8 million (Q1 2022) and $470.1 million (Q3 2021) over five years.
- Compared with a year earlier, Operating Expenses has increased for six straight quarters, with growth averaging 4.4% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q4 2021 (growth of 325.9%); the worst was Q1 2022 (a decline of 95.6%).
- Per Business Quant data, VRSK's Operating Expenses in the three quarters before Q2 2026 was $430.4 million (Q1 2026), $465.2 million (Q4 2025) and $422.4 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 116.38 Bn | 111.98 Bn | 2.98 Bn | 2.35 Bn |
| 2 | Moodys | 78.67 Bn | 71.10 Bn | 1.67 Bn | 1.14 Bn |
| 3 | Msci | 39.16 Bn | 37.50 Bn | 717.10 Mn | 379.50 Mn |
| 4 | Verisk Analytics | 21.84 Bn | 16.48 Bn | 572.90 Mn | 442.60 Mn |
| 5 | Equifax | 16.14 Bn | 15.54 Bn | 926.40 Mn | 1.39 Bn |
| 6 | TransUnion | 11.72 Bn | 8.70 Bn | - | 1.05 Bn |
| 7 | Factset Research Systems | 9.66 Bn | 8.43 Bn | 310.73 Mn | 456.62 Mn |
| 8 | Morningstar | 7.13 Bn | 5.03 Bn | 423.90 Mn | 509.40 Mn |
| 9 | Mastercard | 480.27 Bn | 440.19 Bn | - | 3.69 Bn |
| 10 | Cme | 94.12 Bn | 94.12 Bn | - | 599.10 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 442.60 Mn |
| Mar 31, 2026 | 430.40 Mn |
| Dec 31, 2025 | 465.20 Mn |
| Sep 30, 2025 | 422.40 Mn |
| Jun 30, 2025 | 418.30 Mn |
| Mar 31, 2025 | 422.90 Mn |
| Dec 31, 2024 | 419.30 Mn |
| Sep 30, 2024 | 413.80 Mn |
| Jun 30, 2024 | 398.10 Mn |
| Mar 31, 2024 | 396.60 Mn |
| Dec 31, 2023 | 426.70 Mn |
| Sep 30, 2023 | 396.50 Mn |
| Jun 30, 2023 | 369.00 Mn |
| Mar 31, 2023 | 357.50 Mn |
| Dec 31, 2022 | 348.00 Mn |
| Sep 30, 2022 | 356.50 Mn |
| Jun 30, 2022 | 365.20 Mn |
| Mar 31, 2022 | 20.80 Mn |
| Dec 31, 2021 | 125.20 Mn |
| Sep 30, 2021 | 470.10 Mn |
API Access
Verisk Analytics Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=VRSK&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "VRSK", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=VRSK&period=max&api_key=YOUR_API_KEY");
const data = await res.json();