Infosys (INFY) Accumulated Expenses (2009 - 2026)
Infosys (INFY) posted Accumulated Expenses of $1.0 billion for Q1 2026, up 3.03% on a QoQ basis from $991.0 million in Q1 2025, and up 3.03% year-over-year from $991.0 million in Q1 2025.
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Analysis
Infosys (INFY) Accumulated Expenses (2009 - 2026) Analysis & Trends
Infosys has reported Accumulated Expenses for 18 years, with the latest figure at $1.0 billion in Q1 2026.
- On a quarterly basis, Accumulated Expenses rose 3.03% year-over-year to $1.0 billion in Q1 2026; TTM through Mar 2026 was $1.0 billion, a 3.03% increase from a year earlier, with the FY2026 full-year figure at $1.0 billion, up 3.03% from the prior year.
- Accumulated Expenses was $1.0 billion for Q1 2026 at Infosys, up from $991.0 million in the prior quarter.
- The five-year high for Accumulated Expenses was $1.0 billion in Q1 2026, with the low at $949.0 million in Q1 2023.
- Average Accumulated Expenses over 5 years is $986.6 million, with a median of $986.0 million recorded in 2022.
- The sharpest annual moves came in 2022 and 2023: Accumulated Expenses soared 61.11% in 2022, then fell 3.75% in 2023.
- Over 5 years, Accumulated Expenses stood at $986.0 million in 2022, then fell by 3.75% to $949.0 million in 2023, then climbed by 3.9% to $986.0 million in 2024, then rose by 0.51% to $991.0 million in 2025, then increased by 3.03% to $1.0 billion in 2026.
- The last three Accumulated Expenses figures came in at $1.0 billion (Q1 2026), $991.0 million (Q1 2025), and $986.0 million (Q1 2024), per Business Quant data.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Infosys | 44.17 Bn | 44.22 Bn | 1.60 Bn |
| 2 | Cognizant Technology Solutions | 26.84 Bn | 25.79 Bn | 1.83 Bn |
| 3 | Cgi | 13.60 Bn | 13.02 Bn | - |
| 4 | EPAM Systems | 5.82 Bn | 5.04 Bn | 429.57 Mn |
| 5 | Science Applications International | 5.60 Bn | 5.47 Bn | 239.00 Mn |
| 6 | ExlService Holdings | 5.35 Bn | 5.07 Bn | 225.96 Mn |
| 7 | Kyndryl Holdings | 2.65 Bn | 639.03 Mn | 776.00 Mn |
| 8 | Innodata | 2.10 Bn | 1.85 Bn | 42.46 Mn |
| 9 | Formula Systems (1985) | 1.83 Bn | 1.41 Bn | 158.43 Mn |
| 10 | DXC Technology | 1.74 Bn | 9.39 Mn | 611.00 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2026 | 1.02 Bn |
| Mar 31, 2025 | 991.00 Mn |
| Mar 31, 2024 | 986.00 Mn |
| Mar 31, 2023 | 949.00 Mn |
| Mar 31, 2022 | 986.00 Mn |
| Mar 31, 2021 | 612.00 Mn |
| Mar 31, 2020 | 518.00 Mn |
| Mar 31, 2019 | 480.00 Mn |
| Mar 31, 2018 | 376.00 Mn |
| Mar 31, 2017 | 399.00 Mn |
| Mar 31, 2016 | 10.00 Mn |
| Mar 31, 2015 | 8.00 Mn |
| Dec 31, 2014 | 306.00 Mn |
| Sep 30, 2014 | 280.00 Mn |
| Jun 30, 2014 | 288.00 Mn |
| Mar 31, 2014 | 7.00 Mn |
| Dec 31, 2013 | 6.00 Mn |
| Sep 30, 2013 | 8.00 Mn |
| Jun 30, 2013 | 3.00 Mn |
| Mar 31, 2013 | 6.00 Mn |
API Access
Infosys 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=INFY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "INFY", "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=INFY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();