US Bank Credit
Weekly bank credit, loans & leases, C&I / real-estate / consumer loans, cash assets and deposits across all US commercial banks (Fed H.8) - the private-credit half of the US liquidity suite.
- Source
- FRED
- Licence
- Redistributable open data
- Updates
- Weekly
- Data through
- 2026-07-15
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- 7 Fed H.8 series - Board of Governors origin, all commercial banks, USD bn
- Weekly (SA): bank credit, loans & leases, cash assets, deposits, consumer loans
- Monthly (SA): C&I loans, real-estate loans (H.8 has no weekly all-bank aggregate for these two)
- The private-credit half of the US liquidity suite - complements us-net-liquidity (the Fed's own balance sheet)
- Public domain at origin (Board of Governors) - attribute the Fed, not FRED
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | Always US | 100.0% | 1 |
| ts | timestamp (UTC) | Observation date (week-ending Wednesday or month) | 100.0% | 1,032 |
| series | str | H.8 series (Bank credit, Loans & leases, C&I loans, Real estate loans, Consumer loans, Cash assets, Deposits) | 100.0% | 7 |
| usd_bn | float | Value, USD billions | 100.0% | 4,543 |
| unit | str | USD bn | 100.0% | 1 |
| res_min | int | Native resolution (10080 = weekly, 43200 = monthly) | 100.0% | 2 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: FRED (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-bank-credit_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 1 zones
US
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