Automatic Data Processing (ADP) Accumulated Expenses (2009 - 2026)
Automatic Data Processing (ADP) posted Accumulated Expenses of $3.33 billion for fiscal Q1 2027 (quarter ended Sep 30, 2026), up 14.5% from $2.91 billion a year earlier and up 3.4% from the prior quarter.
Powered by 
Analysis
Automatic Data Processing (ADP) Accumulated Expenses (2009 - 2026) Analysis & Trends
At the end of FY2026 (ended Jun 30, 2026), Automatic Data Processing's Accumulated Expenses came in at $3.22 billion, up 4.1% from FY2025.
- Annual Accumulated Expenses shows a five-year compound annual growth rate of 10.4% (FY2021 to FY2026).
- In prior fiscal years, Automatic Data Processing's Accumulated Expenses was $3.09 billion in FY2025 (-7.7%), $3.35 billion in FY2024 (+43.0%), $2.34 billion in FY2023 (+11.1%) and $2.11 billion in FY2022 (+7.4%).
- The fiscal Q1 2027 figure stands as the highest quarterly Accumulated Expenses since fiscal Q4 2024.
- On a year-over-year basis, Accumulated Expenses has increased in each of the last three quarters, with an average decline of 1.6% over the last eight quarters.
- The strongest year-over-year quarter for Accumulated Expenses in the past five years was fiscal Q3 2024, with growth of 69.7%; the weakest was fiscal Q3 2025, with a decline of 19.8%.
- According to Business Quant data, Accumulated Expenses for the three prior fiscal quarters was $3.22 billion (Q4 2026), $3.01 billion (Q3 2026) and $2.48 billion (Q2 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palantir Technologies | 413.65 Bn | 378.59 Bn | 1.93 Bn |
| 2 | Oracle | 374.28 Bn | 246.84 Bn | - |
| 3 | Sap Se | 294.90 Bn | 222.88 Bn | 8.64 Bn |
| 4 | Salesforce | 186.99 Bn | 142.87 Bn | 8.70 Bn |
| 5 | Automatic Data Processing | 120.08 Bn | 106.87 Bn | 2.53 Bn |
| 6 | ServiceNow | 113.02 Bn | 92.28 Bn | 2.90 Bn |
| 7 | Intuit | 95.72 Bn | 75.07 Bn | 3.44 Bn |
| 8 | Relx | 74.73 Bn | 73.84 Bn | - |
| 9 | Strategy | 57.35 Bn | 44.49 Bn | 89.63 Mn |
| 10 | Thomson Reuters | 52.16 Bn | 48.13 Bn | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 3.33 Bn |
| Jun 30, 2026 | 3.22 Bn |
| Mar 31, 2026 | 3.01 Bn |
| Dec 31, 2025 | 2.48 Bn |
| Sep 30, 2025 | 2.91 Bn |
| Jun 30, 2025 | 3.09 Bn |
| Mar 31, 2025 | 2.98 Bn |
| Dec 31, 2024 | 3.09 Bn |
| Sep 30, 2024 | 2.97 Bn |
| Jun 30, 2024 | 3.35 Bn |
| Mar 31, 2024 | 3.71 Bn |
| Dec 31, 2023 | 2.63 Bn |
| Sep 30, 2023 | 2.29 Bn |
| Jun 30, 2023 | 2.34 Bn |
| Mar 31, 2023 | 2.19 Bn |
| Dec 31, 2022 | 2.37 Bn |
| Sep 30, 2022 | 2.17 Bn |
| Jun 30, 2022 | 2.11 Bn |
| Mar 31, 2022 | 2.10 Bn |
| Dec 31, 2021 | 1.94 Bn |
API Access
Automatic Data Processing Accumulated 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=accumulated-expenses&ticker=ADP&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "ADP", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=ADP&period=max&api_key=YOUR_API_KEY");
const data = await res.json();