Diebold Nixdorf (DBD) Total Non-Current Liabilities (2009 - 2026)
Diebold Nixdorf's Total Non-Current Liabilities came in at $2.6 billion for Q2 2026, up 3.4% from $2.52 billion a year earlier but down 2.0% from the prior quarter.
Diebold Nixdorf (DBD) Total Non-Current Liabilities (2009 - 2026) Analysis & Trends
At the end of FY2025, Diebold Nixdorf's Total Non-Current Liabilities was $2.63 billion, up 5.5% from FY2024.
- Total Non-Current Liabilities carries a five-year compound annual growth rate of -9.9% (FY2020 to FY2025).
- Going back by year, Total Non-Current Liabilities was $2.49 billion in FY2024 (-18.4%), $3.06 billion in FY2023 (-30.6%), $4.4 billion in FY2022 (+1.4%) and $4.34 billion in FY2021 (-1.9%).
- The five-year range for quarterly Total Non-Current Liabilities is $2.49 billion (Q3 2025) to $4.4 billion (Q4 2022).
- Year-over-year, Total Non-Current Liabilities has increased for three consecutive quarters, with an average decline of 4.7% over the last seven quarters.
- The fastest year-over-year change in Total Non-Current Liabilities over five years came in Q4 2025 (growth of 5.5%), and the weakest in Q4 2023 (a decline of 30.6%).
- Business Quant data shows DBD's Total Non-Current Liabilities at $2.66 billion (Q1 2026), $2.63 billion (Q4 2025) and $2.49 billion (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Bryn | 945.00 Bn | 945.00 Bn | - | - |
| 2 | Veeva Systems | 45.35 Bn | 17.59 Bn | 695.95 Mn | 1.60 Bn |
| 3 | Samsara | 22.20 Bn | 18.97 Bn | 392.58 Mn | 1.15 Bn |
| 4 | Toast | 17.53 Bn | 10.20 Bn | 516.00 Mn | 1.10 Bn |
| 5 | Ptc | 15.47 Bn | 14.28 Bn | 490.47 Mn | 2.94 Bn |
| 6 | Trimble | 13.41 Bn | 12.48 Bn | 674.90 Mn | 3.15 Bn |
| 7 | Duolingo | 12.56 Bn | 7.73 Bn | 216.74 Mn | - |
| 8 | Manhattan Associates | 11.77 Bn | 10.77 Bn | 168.33 Mn | 528.94 Mn |
| 9 | Costar | 10.95 Bn | 4.91 Bn | 728.00 Mn | 2.16 Bn |
| 10 | Diebold Nixdorf | 2.07 Bn | 741.27 Mn | 239.60 Mn | 2.60 Bn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 2.60 Bn |
| Mar 31, 2026 | 2.66 Bn |
| Dec 31, 2025 | 2.63 Bn |
| Sep 30, 2025 | 2.49 Bn |
| Jun 30, 2025 | 2.52 Bn |
| Mar 31, 2025 | 2.60 Bn |
| Dec 31, 2024 | 2.49 Bn |
| Sep 30, 2024 | 2.73 Bn |
| Jun 30, 2024 | 2.76 Bn |
| Mar 31, 2024 | 2.83 Bn |
| Dec 31, 2023 | 3.06 Bn |
| Sep 30, 2023 | 2.91 Bn |
| Jun 30, 2023 | 5.52 Bn |
| Mar 31, 2023 | 4.54 Bn |
| Dec 31, 2022 | 4.40 Bn |
| Sep 30, 2022 | 4.11 Bn |
| Jun 30, 2022 | 4.30 Bn |
| Mar 31, 2022 | 4.33 Bn |
| Dec 31, 2021 | 4.34 Bn |
| Sep 30, 2021 | 4.30 Bn |
Diebold Nixdorf Total Non-Current Liabilities 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=total-non-current-liabilities&ticker=DBD&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-current-liabilities", "ticker": "DBD", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=total-non-current-liabilities&ticker=DBD&period=max&api_key=YOUR_API_KEY");
const data = await res.json();