American Express (AXP) Operating Expenses (2009 - 2026)
American Express (AXP) reported Operating Expenses of $14.48 billion for Q2 2026, up 12.3% from $12.9 billion a year earlier and up 4.4% from the prior quarter.
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Analysis
American Express (AXP) Operating Expenses (2009 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, American Express' Operating Expenses came in at $56.15 billion, up 11.0% year-over-year; for FY2025, it was $53.18 billion, up 11.1% from FY2024.
- Operating Expenses has increased for five consecutive years, with a five-year compound annual growth rate of 14.5% (FY2020 to FY2025).
- By year, Operating Expenses came in at $47.87 billion in FY2024 (+6.2%), $45.08 billion in FY2023 (+9.7%), $41.1 billion in FY2022 (+24.1%) and $33.11 billion in FY2021 (+22.4%).
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses in data going back to Q1 2009.
- Year over year, Operating Expenses has now increased in each of the last 21 quarters, with growth averaging 11.0% over the last eight quarters.
- Over the past five years, the year-over-year growth in Operating Expenses ranged from 1.4% (Q2 2024) to 34.2% (Q1 2022).
- Per Business Quant data, the three quarters before Q2 2026 came in at $13.88 billion (Q1 2026), $14.48 billion (Q4 2025) and $13.31 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 885.23 Bn | 914.70 Bn | - | 27.32 Bn |
| 2 | Banco Santander Chile | 404.41 Bn | 524.35 Bn | - | -3,218.32 Bn |
| 3 | Bank Of America | 381.15 Bn | -1,994.92 Bn | - | 18.63 Bn |
| 4 | Hsbc Holdings | 319.39 Bn | 319.44 Bn | - | - |
| 5 | Morgan Stanley | 298.67 Bn | -210.43 Bn | - | 13.90 Bn |
| 6 | Royal Bank Of Canada | 268.75 Bn | -557.52 Bn | - | 7.02 Bn |
| 7 | Mitsubishi Ufj Financial | 265.72 Bn | -107.91 Bn | 10.89 Bn | 17.50 Bn |
| 8 | Goldman Sachs | 261.04 Bn | -3,296.07 Bn | - | 11.67 Bn |
| 9 | Wells Fargo & Company | 253.09 Bn | 255.22 Bn | - | 13.66 Bn |
| 10 | American Express | 208.01 Bn | -160.24 Mn | - | 14.48 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 14.48 Bn |
| Mar 31, 2026 | 13.88 Bn |
| Dec 31, 2025 | 14.48 Bn |
| Sep 30, 2025 | 13.31 Bn |
| Jun 30, 2025 | 12.90 Bn |
| Mar 31, 2025 | 12.49 Bn |
| Dec 31, 2024 | 13.13 Bn |
| Sep 30, 2024 | 12.08 Bn |
| Jun 30, 2024 | 11.28 Bn |
| Mar 31, 2024 | 11.39 Bn |
| Dec 31, 2023 | 11.85 Bn |
| Sep 30, 2023 | 11.05 Bn |
| Jun 30, 2023 | 11.12 Bn |
| Mar 31, 2023 | 11.06 Bn |
| Dec 31, 2022 | 11.28 Bn |
| Sep 30, 2022 | 10.32 Bn |
| Jun 30, 2022 | 10.44 Bn |
| Mar 31, 2022 | 9.06 Bn |
| Dec 31, 2021 | 9.79 Bn |
| Sep 30, 2021 | 8.67 Bn |
API Access
American Express 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=AXP&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "AXP", "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=AXP&period=max&api_key=YOUR_API_KEY");
const data = await res.json();