Willis Towers Watson (WTW) Operating Expenses (2009 - 2026)
Willis Towers Watson's Operating Expenses was $2.1 billion in Q2 2026, up 11.0% from $1.89 billion a year earlier and up 7.0% from the prior quarter.
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Analysis
Willis Towers Watson (WTW) Operating Expenses (2009 - 2026) Analysis & Trends
On a trailing twelve-month basis, Willis Towers Watson's Operating Expenses was $7.86 billion through Jun 30, 2026, down 11.5% year-over-year; for FY2025, it was $7.47 billion, down 19.7% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of -0.7% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $9.3 billion in FY2024 (+14.6%), $8.12 billion in FY2023 (+5.6%), $7.69 billion in FY2022 (+13.1%) and $6.8 billion in FY2021 (-12.4%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses since Q4 2024.
- Compared with a year earlier, Operating Expenses was higher in three of the last eight quarters, with growth averaging 0.4%.
- The best year-over-year quarter for Operating Expenses over five years was Q3 2022 (growth of 113.7%); the worst was Q3 2021 (a decline of 54.0%).
- Per Business Quant data, WTW's Operating Expenses in the three quarters before Q2 2026 was $1.96 billion (Q1 2026), $1.92 billion (Q4 2025) and $1.87 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Chubb | 137.65 Bn | 84.23 Bn | 9.13 Bn | 12.19 Bn |
| 2 | Progressive | 129.27 Bn | 105.11 Bn | 9.04 Bn | 19.40 Bn |
| 3 | Marsh & Mclennan Companies | 90.79 Bn | 82.95 Bn | - | 5.65 Bn |
| 4 | Travelers Companies | 83.98 Bn | 60.23 Bn | 6.23 Bn | 9.39 Bn |
| 5 | Manulife Financial | 77.09 Bn | -17.80 Bn | - | -902.21 Mn |
| 6 | Arthur J. Gallagher | 66.40 Bn | 60.80 Bn | - | 3.59 Bn |
| 7 | Metlife | 65.77 Bn | -41.01 Bn | 7.82 Bn | 18.12 Bn |
| 8 | Allstate | 64.29 Bn | 25.56 Bn | 8.73 Bn | 14.45 Bn |
| 9 | Aon | 62.89 Bn | 56.11 Bn | - | 3.33 Bn |
| 10 | Willis Towers Watson | 29.88 Bn | 21.29 Bn | - | 2.10 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 2.10 Bn |
| Mar 31, 2026 | 1.96 Bn |
| Dec 31, 2025 | 1.92 Bn |
| Sep 30, 2025 | 1.87 Bn |
| Jun 30, 2025 | 1.89 Bn |
| Mar 31, 2025 | 1.79 Bn |
| Dec 31, 2024 | 2.13 Bn |
| Sep 30, 2024 | 3.06 Bn |
| Jun 30, 2024 | 2.05 Bn |
| Mar 31, 2024 | 2.06 Bn |
| Dec 31, 2023 | 2.14 Bn |
| Sep 30, 2023 | 2.01 Bn |
| Jun 30, 2023 | 2.02 Bn |
| Mar 31, 2023 | 1.96 Bn |
| Dec 31, 2022 | 2.01 Bn |
| Sep 30, 2022 | 1.80 Bn |
| Jun 30, 2022 | 1.89 Bn |
| Mar 31, 2022 | 1.98 Bn |
| Dec 31, 2021 | 2.02 Bn |
| Sep 30, 2021 | 842.00 Mn |
API Access
Willis Towers Watson 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=WTW&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "WTW", "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=WTW&period=max&api_key=YOUR_API_KEY");
const data = await res.json();