Wsfs Financial (WSFS) Operating Expenses (2010 - 2026)
Wsfs Financial's Operating Expenses was $166.3 million in Q2 2026, up 4.4% from $159.34 million a year earlier and up 2.2% from the prior quarter.
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Analysis
Wsfs Financial (WSFS) Operating Expenses (2010 - 2026) Analysis & Trends
On a trailing twelve-month basis, Wsfs Financial's Operating Expenses was $654.09 million through Jun 30, 2026, up 1.6% year-over-year; for FY2025, it came in at $636.17 million, down 0.2% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of 11.5% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $637.69 million in FY2024 (+13.5%), $561.63 million in FY2023 (-2.2%), $574.33 million in FY2022 (+51.7%) and $378.52 million in FY2021 (+2.6%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses since Q4 2024.
- Compared with a year earlier, Operating Expenses was higher in six of the last eight quarters, with growth averaging 5.4%.
- The best year-over-year quarter for Operating Expenses over five years was Q1 2022 (growth of 82.5%); the worst was Q1 2023 (a decline of 23.7%).
- Per Business Quant data, WSFS's Operating Expenses in the three quarters before Q2 2026 was $162.77 million (Q1 2026), $161.97 million (Q4 2025) and $163.06 million (Q3 2025).
Peer Set
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 | Wsfs Financial | 3.86 Bn | -4.46 Bn | - | 166.30 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 166.30 Mn |
| Mar 31, 2026 | 162.77 Mn |
| Dec 31, 2025 | 161.97 Mn |
| Sep 30, 2025 | 163.06 Mn |
| Jun 30, 2025 | 159.34 Mn |
| Mar 31, 2025 | 151.80 Mn |
| Dec 31, 2024 | 169.13 Mn |
| Sep 30, 2024 | 163.72 Mn |
| Jun 30, 2024 | 155.77 Mn |
| Mar 31, 2024 | 149.07 Mn |
| Dec 31, 2023 | 147.65 Mn |
| Sep 30, 2023 | 139.69 Mn |
| Jun 30, 2023 | 141.25 Mn |
| Mar 31, 2023 | 133.05 Mn |
| Dec 31, 2022 | 132.90 Mn |
| Sep 30, 2022 | 132.92 Mn |
| Jun 30, 2022 | 134.05 Mn |
| Mar 31, 2022 | 174.46 Mn |
| Dec 31, 2021 | 90.42 Mn |
| Sep 30, 2021 | 96.45 Mn |
API Access
Wsfs 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=WSFS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "WSFS", "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=WSFS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();