Enterprise Financial Services (EFSC) Operating Expenses (2010 - 2026)
Enterprise Financial Services (EFSC) recorded Operating Expenses of $115.74 million in Q2 2026, up 9.5% from $105.7 million a year earlier and up 0.5% from the prior quarter.
Enterprise Financial Services (EFSC) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Enterprise Financial Services' Operating Expenses came in at $455.2 million as of Jun 30, 2026, up 12.9% year-over-year; for FY2025, it came in at $429.81 million, up 11.6% from FY2024.
- Annual Operating Expenses has increased for ten straight years, with a five-year compound annual growth rate of 20.8% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $385.05 million in FY2024 (+10.6%), $348.19 million in FY2023 (+27.0%), $274.22 million in FY2022 (+11.5%) and $245.92 million in FY2021 (+47.1%).
- The Q2 2026 figure is the highest quarterly Operating Expenses in data going back to Q2 2010.
- On a year-over-year basis, Operating Expenses has increased for 15 consecutive quarters, with growth averaging 11.1% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 94.5% in Q3 2021, against a decline of 10.5% in Q3 2022 at the low end.
- Per Business Quant, the preceding three quarters came in at $115.14 million (Q1 2026), $114.53 million (Q4 2025) and $109.79 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 879.54 Bn | 909.01 Bn | - | 27.32 Bn |
| 2 | Banco Santander Chile | 418.48 Bn | 538.42 Bn | - | -3,218.32 Bn |
| 3 | Bank Of America | 381.92 Bn | -1,994.15 Bn | - | 18.63 Bn |
| 4 | Hsbc Holdings | 341.10 Bn | 341.15 Bn | - | - |
| 5 | Morgan Stanley | 295.71 Bn | -213.38 Bn | - | 13.90 Bn |
| 6 | Royal Bank Of Canada | 275.38 Bn | 126.13 Bn | - | 7.02 Bn |
| 7 | Mitsubishi Ufj Financial | 273.79 Bn | -1,314.70 Bn | 10.89 Bn | 17.50 Bn |
| 8 | Goldman Sachs | 262.78 Bn | -3,294.34 Bn | - | 11.67 Bn |
| 9 | Wells Fargo & Company | 242.61 Bn | 244.75 Bn | - | 13.66 Bn |
| 10 | Enterprise Financial Services | 2.14 Bn | 2.14 Bn | - | 115.74 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 115.74 Mn |
| Mar 31, 2026 | 115.14 Mn |
| Dec 31, 2025 | 114.53 Mn |
| Sep 30, 2025 | 109.79 Mn |
| Jun 30, 2025 | 105.70 Mn |
| Mar 31, 2025 | 99.78 Mn |
| Dec 31, 2024 | 99.52 Mn |
| Sep 30, 2024 | 98.01 Mn |
| Jun 30, 2024 | 94.02 Mn |
| Mar 31, 2024 | 93.50 Mn |
| Dec 31, 2023 | 92.60 Mn |
| Sep 30, 2023 | 88.64 Mn |
| Jun 30, 2023 | 85.96 Mn |
| Mar 31, 2023 | 80.98 Mn |
| Dec 31, 2022 | 77.15 Mn |
| Sep 30, 2022 | 68.84 Mn |
| Jun 30, 2022 | 65.42 Mn |
| Mar 31, 2022 | 62.80 Mn |
| Dec 31, 2021 | 63.69 Mn |
| Sep 30, 2021 | 76.89 Mn |
Enterprise Financial Services 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=EFSC&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "EFSC", "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=EFSC&period=max&api_key=YOUR_API_KEY");
const data = await res.json();