Pitney Bowes (PBI) Operating Interest Expenses (2009 - 2026)
Pitney Bowes (PBI) posted Operating Interest Expenses of $12.42 million for Q2 2026, down 20.6% from $15.66 million a year earlier and down 2.9% from the prior quarter.
Pitney Bowes (PBI) Operating Interest Expenses (2009 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Interest Expenses at Pitney Bowes was $53.56 million, down 26.2% year-over-year; for FY2025, it was $61.5 million, down 24.1% from FY2024.
- Annual Operating Interest Expenses shows a five-year compound annual growth rate of -23.5% (FY2020 to FY2025).
- In prior years, Pitney Bowes' Operating Interest Expenses was $81.06 million in FY2024 (-2.2%), $82.9 million in FY2023 (+60.1%), $51.79 million in FY2022 (-79.4%) and $251.91 million in FY2021 (+7.1%).
- The Q2 2026 figure stands as the lowest quarterly Operating Interest Expenses since Q3 2019.
- On a year-over-year basis, Operating Interest Expenses has declined in each of the last seven quarters, with an average decline of 21.6% over the last eight quarters.
- The strongest year-over-year quarter for Operating Interest Expenses in the past five years was Q4 2023, with growth of 163.5%; the weakest was Q4 2022, with a decline of 78.9%.
- According to Business Quant data, Operating Interest Expenses for the three prior quarters was $12.8 million (Q1 2026), $13.63 million (Q4 2025) and $14.71 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Op. Interest Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Accenture | 122.50 Bn | 82.91 Bn | 6.13 Bn | - |
| 2 | Cintas | 78.05 Bn | 77.24 Bn | 1.48 Bn | - |
| 3 | Iron Mountain | 33.02 Bn | 32.53 Bn | 1.07 Bn | - |
| 4 | APi | 16.68 Bn | 13.72 Bn | 703.00 Mn | - |
| 5 | Rollins | 14.49 Bn | 14.04 Bn | 569.95 Mn | - |
| 6 | Aramark | 14.11 Bn | 12.13 Bn | 430.34 Mn | - |
| 7 | UL Solutions | 13.27 Bn | 12.05 Bn | 417.00 Mn | - |
| 8 | Gartner | 11.78 Bn | 5.47 Bn | 1.19 Bn | - |
| 9 | Rentokil Initial | 9.98 Bn | 3.32 Bn | - | - |
| 10 | Pitney Bowes | 2.24 Bn | 1.01 Bn | 254.70 Mn | 12.42 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 12.42 Mn |
| Mar 31, 2026 | 12.80 Mn |
| Dec 31, 2025 | 13.63 Mn |
| Sep 30, 2025 | 14.71 Mn |
| Jun 30, 2025 | 15.66 Mn |
| Mar 31, 2025 | 17.51 Mn |
| Dec 31, 2024 | 19.20 Mn |
| Sep 30, 2024 | 20.17 Mn |
| Jun 30, 2024 | 20.40 Mn |
| Mar 31, 2024 | 21.29 Mn |
| Dec 31, 2023 | 36.78 Mn |
| Sep 30, 2023 | 16.81 Mn |
| Jun 30, 2023 | 14.76 Mn |
| Mar 31, 2023 | 14.54 Mn |
| Dec 31, 2022 | 13.96 Mn |
| Sep 30, 2022 | 13.69 Mn |
| Jun 30, 2022 | 63.82 Mn |
| Mar 31, 2022 | 63.77 Mn |
| Dec 31, 2021 | 66.29 Mn |
| Sep 30, 2021 | 62.22 Mn |
Pitney Bowes Operating Interest 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-interest-expenses&ticker=PBI&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-interest-expenses", "ticker": "PBI", "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-interest-expenses&ticker=PBI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();