Alerus Financial (ALRS) Operating Expenses (2018 - 2026)
Alerus Financial's Operating Expenses was $52.88 million in Q2 2026, up 9.2% from $48.44 million a year earlier and up 4.9% from the prior quarter.
Alerus Financial (ALRS) Operating Expenses (2018 - 2026) Analysis & Trends
On a trailing twelve-month basis, Alerus Financial's Operating Expenses was $205.7 million through Jun 30, 2026, up 2.0% year-over-year; for FY2025, it was $201.23 million, up 11.4% from FY2024.
- Operating Expenses shows a four-year compound annual growth rate of 4.5% (FY2021 to FY2025).
- In earlier years, Operating Expenses was $180.68 million in FY2024 (+20.3%), $150.16 million in FY2023 (-5.4%), $158.77 million in FY2022 (-6.0%) and $168.91 million in FY2021.
- The Q2 2026 figure marks the highest quarterly Operating Expenses since Q4 2024.
- Compared with a year earlier, Operating Expenses was higher in seven of the last eight quarters, with growth averaging 17.3%.
- The best year-over-year quarter for Operating Expenses over five years was Q4 2024 (growth of 56.4%); the worst was Q4 2025 (a decline of 14.2%).
- Per Business Quant data, ALRS's Operating Expenses in the three quarters before Q2 2026 was $50.39 million (Q1 2026), $51.88 million (Q4 2025) and $50.54 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 890.60 Bn | 920.07 Bn | - | 27.32 Bn |
| 2 | Banco Santander Chile | 421.74 Bn | 541.68 Bn | - | -3,218.32 Bn |
| 3 | Bank Of America | 385.85 Bn | -1,990.22 Bn | - | 18.63 Bn |
| 4 | Hsbc Holdings | 345.94 Bn | 345.99 Bn | - | - |
| 5 | Morgan Stanley | 303.15 Bn | -205.95 Bn | - | 13.90 Bn |
| 6 | Royal Bank Of Canada | 279.82 Bn | 130.57 Bn | - | 7.02 Bn |
| 7 | Mitsubishi Ufj Financial | 274.62 Bn | -1,313.87 Bn | 10.89 Bn | 17.50 Bn |
| 8 | Goldman Sachs | 267.07 Bn | -3,290.04 Bn | - | 11.67 Bn |
| 9 | Wells Fargo & Company | 243.79 Bn | 245.93 Bn | - | 13.66 Bn |
| 10 | Alerus Financial | 793.80 Mn | 391.21 Mn | - | 52.88 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 52.88 Mn |
| Mar 31, 2026 | 50.39 Mn |
| Dec 31, 2025 | 51.88 Mn |
| Sep 30, 2025 | 50.54 Mn |
| Jun 30, 2025 | 48.44 Mn |
| Mar 31, 2025 | 50.37 Mn |
| Dec 31, 2024 | 60.46 Mn |
| Sep 30, 2024 | 42.45 Mn |
| Jun 30, 2024 | 38.75 Mn |
| Mar 31, 2024 | 39.02 Mn |
| Dec 31, 2023 | 38.65 Mn |
| Sep 30, 2023 | 37.26 Mn |
| Jun 30, 2023 | 36.37 Mn |
| Mar 31, 2023 | 37.87 Mn |
| Dec 31, 2022 | 37.95 Mn |
| Sep 30, 2022 | 42.77 Mn |
| Jun 30, 2022 | 39.98 Mn |
| Mar 31, 2022 | 38.07 Mn |
| Dec 31, 2021 | 41.28 Mn |
| Sep 30, 2021 | 42.04 Mn |
Alerus Financial 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=ALRS&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ALRS", "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=ALRS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();