iHuman (IH) Total Liabilities (2019 - 2026)
iHuman (IH) reported Total Liabilities of $53.18 million for the quarter ended Mar 31, 2026, down 10.3% from $59.31 million a year earlier but up 1.1% from the prior quarter.
iHuman (IH) Total Liabilities (2019 - 2026) Analysis & Trends
As of Dec 31, 2025, iHuman posted Total Liabilities of $52.62 million, down 46.5% from the prior year.
- Total Liabilities has a five-year compound annual growth rate of -20.3% (years ended Dec 2020 to Dec 2025).
- By year, Total Liabilities came in at $98.41 million in the year ended Dec 31, 2024 (+43.8%), $68.43 million in the year ended Dec 31, 2023 (-16.3%), $81.77 million in the year ended Dec 31, 2022 (+5.8%) and $77.29 million in the year ended Dec 31, 2021 (-52.8%).
- Five-year quarterly Total Liabilities spans a low of $50.56 million in the quarter ended Jun 30, 2025 and a high of $53.75 billion in the quarter ended Sep 30, 2025.
- Year over year, Total Liabilities gained in two of the last seven quarters, with an average decline of 6.9%.
- The high point for year-over-year Total Liabilities in five years was the quarter ended Sep 30, 2021 (growth of 128.2%); the low point was the quarter ended Dec 31, 2025 (a decline of 46.5%).
- Per Business Quant data, the three quarters before the quarter ended Mar 31, 2026 came in at $52.62 million (quarter ended Dec 31, 2025), $53.75 billion (quarter ended Sep 30, 2025) and $50.56 million (quarter ended Jun 30, 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Bryn | 945.00 Bn | 945.00 Bn | - | 206,169.00 |
| 2 | Veeva Systems | 45.35 Bn | 17.59 Bn | 695.95 Mn | 1.63 Bn |
| 3 | Samsara | 22.20 Bn | 18.97 Bn | 392.58 Mn | 1.17 Bn |
| 4 | Toast | 17.53 Bn | 10.20 Bn | 516.00 Mn | 1.14 Bn |
| 5 | Ptc | 15.47 Bn | 14.28 Bn | 490.47 Mn | 3.04 Bn |
| 6 | Trimble | 13.41 Bn | 12.48 Bn | 674.90 Mn | 3.43 Bn |
| 7 | Duolingo | 12.56 Bn | 7.73 Bn | 216.74 Mn | 664.21 Mn |
| 8 | Manhattan Associates | 11.77 Bn | 10.77 Bn | 168.33 Mn | 541.14 Mn |
| 9 | Costar | 10.95 Bn | 4.91 Bn | 728.00 Mn | 2.19 Bn |
| 10 | iHuman | 117.55 Mn | -157.98 Bn | 17.68 Mn | 53.18 Mn |
Historic Data
| Date | Value |
|---|---|
| Mar 31, 2026 | 53.18 Mn |
| Dec 31, 2025 | 52.62 Mn |
| Sep 30, 2025 | 53.75 Bn |
| Jun 30, 2025 | 50.56 Mn |
| Mar 31, 2025 | 59.31 Mn |
| Dec 31, 2024 | 98.41 Mn |
| Sep 30, 2024 | 68.97 Mn |
| Jun 30, 2024 | 62.13 Mn |
| Mar 31, 2024 | 68.94 Mn |
| Dec 31, 2023 | 68.43 Mn |
| Sep 30, 2023 | 66.84 Mn |
| Jun 30, 2023 | 66.22 Mn |
| Mar 31, 2023 | 72.51 Mn |
| Dec 31, 2022 | 79.29 Mn |
| Sep 30, 2022 | 70.95 Mn |
| Jun 30, 2022 | 69.88 Mn |
| Mar 31, 2022 | 74.96 Mn |
| Dec 31, 2021 | 77.03 Mn |
| Sep 30, 2021 | 86.90 Mn |
| Jun 30, 2021 | 75.82 Mn |
iHuman Total 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-liabilities&ticker=IH&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "IH", "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-liabilities&ticker=IH&period=max&api_key=YOUR_API_KEY");
const data = await res.json();