Verisk Analytics (VRSK) Amortization of Intangibles (2010 - 2026)
Verisk Analytics (VRSK) recorded Amortizatization of Intangibles of $14.3 million in Q2 2026, down 12.3% from $16.3 million a year earlier and down 0.7% from the prior quarter.
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Analysis
Verisk Analytics (VRSK) Amortization of Intangibles (2010 - 2026) Analysis & Trends
On a TTM basis, Verisk Analytics' Amortizatization of Intangibles came in at $64.1 million as of Jun 30, 2026, down 5.3% year-over-year; for FY2025, it was $67.5 million, down 6.6% from FY2024.
- Annual Amortizatization of Intangibles has declined for four straight years, with a five-year compound annual growth rate of -16.5% (FY2020 to FY2025).
- Across earlier years, Amortizatization of Intangibles came in at $72.3 million in FY2024 (-3.1%), $74.6 million in FY2023 (-47.8%), $142.9 million in FY2022 (-19.1%) and $176.7 million in FY2021 (+6.5%).
- The Q2 2026 figure is the lowest quarterly Amortizatization of Intangibles since Q1 2015.
- On a year-over-year basis, Amortizatization of Intangibles has declined for three consecutive quarters, with an average decline of 7.6% over the last eight quarters.
- Peak year-over-year performance for Amortizatization of Intangibles in the last five years was growth of 4.5% in Q1 2024, against a decline of 60.3% in Q1 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $14.4 million (Q1 2026), $17.1 million (Q4 2025) and $18.3 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Amort. of Intangibles (Qtr) |
|---|---|---|---|---|---|
| 1 | S&P Global | 137.65 Bn | 127.50 Bn | 2.64 Bn | 199.00 Mn |
| 2 | Moodys | 87.89 Bn | 80.82 Bn | 1.50 Bn | - |
| 3 | Msci | 46.89 Bn | 45.37 Bn | 739.80 Mn | 45.20 Mn |
| 4 | Verisk Analytics | 26.47 Bn | 21.10 Bn | 572.90 Mn | 14.30 Mn |
| 5 | Equifax | 22.36 Bn | 21.63 Bn | 940.10 Mn | - |
| 6 | TransUnion | 15.91 Bn | 12.60 Bn | - | - |
| 7 | Factset Research Systems | 11.95 Bn | 10.81 Bn | 308.40 Mn | - |
| 8 | Morningstar | 9.25 Bn | 7.15 Bn | 431.40 Mn | - |
| 9 | Mastercard | 581.41 Bn | 541.50 Bn | - | - |
| 10 | Cme | 109.03 Bn | 97.37 Bn | - | 56.00 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 14.30 Mn |
| Mar 31, 2026 | 14.40 Mn |
| Dec 31, 2025 | 17.10 Mn |
| Sep 30, 2025 | 18.30 Mn |
| Jun 30, 2025 | 16.30 Mn |
| Mar 31, 2025 | 15.80 Mn |
| Dec 31, 2024 | 17.30 Mn |
| Sep 30, 2024 | 18.30 Mn |
| Jun 30, 2024 | 18.20 Mn |
| Mar 31, 2024 | 18.50 Mn |
| Dec 31, 2023 | 18.50 Mn |
| Sep 30, 2023 | 19.60 Mn |
| Jun 30, 2023 | 18.80 Mn |
| Mar 31, 2023 | 17.70 Mn |
| Dec 31, 2022 | 21.90 Mn |
| Sep 30, 2022 | 36.60 Mn |
| Jun 30, 2022 | 39.80 Mn |
| Mar 31, 2022 | 44.60 Mn |
| Dec 31, 2021 | 43.60 Mn |
| Sep 30, 2021 | 37.60 Mn |
API Access
Verisk Analytics Amortization of Intangibles 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=amortizatization-of-intangibles&ticker=VRSK&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "amortizatization-of-intangibles", "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=amortizatization-of-intangibles&ticker=VRSK&period=max&api_key=YOUR_API_KEY");
const data = await res.json();