Us Bancorp \De\ (USB) Provision for Losses (2010 - 2026)
Us Bancorp's Provision for Losses came in at $654 million for Q3 2026, up 14.5% from $571 million a year earlier and up 21.6% from the prior quarter.
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Analysis
Us Bancorp \De\ (USB) Provision for Losses (2010 - 2026) Analysis & Trends
Over the trailing twelve months to Sep 30, 2026, Us Bancorp reported Provision for Losses of $2.35 billion, up 8.1% year-over-year; for FY2025, it came in at $2.19 billion, down 2.3% from FY2024.
- Provision for Losses carries a five-year compound annual growth rate of -10.5% (FY2020 to FY2025).
- Going back by year, Provision for Losses was $2.24 billion in FY2024 (-1.6%), $2.28 billion in FY2023 (+15.1%), $1.98 billion in FY2022 and -$1.17 billion in FY2021.
- The Q3 2026 figure represents the highest quarterly Provision for Losses since Q2 2023.
- Year-over-year, Provision for Losses has increased for five consecutive quarters, with growth averaging 3.7% over the last eight quarters.
- The fastest year-over-year change in Provision for Losses over five years came in Q1 2023 (growth of 281.3%), and the weakest in Q4 2023 (a decline of 57.0%).
- Business Quant data shows USB's Provision for Losses at $538 million (Q2 2026), $576 million (Q1 2026) and $577 million (Q4 2025) in the three quarters before Q3 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Provision for Losses (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 920.14 Bn | 897.90 Bn | - | 2.74 Bn |
| 2 | Banco Santander Chile | 407.42 Bn | -7,109.68 Bn | - | - |
| 3 | Bank Of America | 353.20 Bn | -2,022.91 Bn | - | 1.30 Bn |
| 4 | Hsbc Holdings | 307.88 Bn | -2,967.28 Bn | - | - |
| 5 | Morgan Stanley | 298.41 Bn | -244.41 Bn | - | - |
| 6 | Royal Bank Of Canada | 283.46 Bn | -546.54 Bn | - | 717.20 Mn |
| 7 | Goldman Sachs | 264.45 Bn | -3,478.99 Bn | - | 368.00 Mn |
| 8 | Mitsubishi Ufj Financial | 259.19 Bn | -159.36 Bn | 5.24 Bn | - |
| 9 | Wells Fargo & Company | 258.28 Bn | 93.72 Bn | - | 1.03 Bn |
| 10 | Us Bancorp \De\ | 95.04 Bn | 95.50 Bn | - | 654.00 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 654.00 Mn |
| Jun 30, 2026 | 538.00 Mn |
| Mar 31, 2026 | 576.00 Mn |
| Dec 31, 2025 | 577.00 Mn |
| Sep 30, 2025 | 571.00 Mn |
| Jun 30, 2025 | 501.00 Mn |
| Mar 31, 2025 | 537.00 Mn |
| Dec 31, 2024 | 560.00 Mn |
| Sep 30, 2024 | 557.00 Mn |
| Jun 30, 2024 | 568.00 Mn |
| Mar 31, 2024 | 553.00 Mn |
| Dec 31, 2023 | 512.00 Mn |
| Sep 30, 2023 | 515.00 Mn |
| Jun 30, 2023 | 821.00 Mn |
| Mar 31, 2023 | 427.00 Mn |
| Dec 31, 2022 | 1.19 Bn |
| Sep 30, 2022 | 362.00 Mn |
| Jun 30, 2022 | 311.00 Mn |
| Mar 31, 2022 | 112.00 Mn |
| Dec 31, 2021 | -13.00 Mn |
API Access
Us Bancorp \De\ Provision for Losses 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=provision-for-losses&ticker=USB&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "provision-for-losses", "ticker": "USB", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=provision-for-losses&ticker=USB&period=max&api_key=YOUR_API_KEY");
const data = await res.json();