US loan-loss signals (charge-off rates, stress regimes)
Quarterly US bank loan-loss signals from Federal Reserve Board charge-off rates (via FRED): all-real-estate-loan charge-offs (CORALACBS) and credit-card charge-offs (CORCCACBS), 1991 ->, with quarter-on-quarter and year-on-year change, 30-quarter change volatility, 3-sigma anomaly flags, naive-drift forecasts, 5-year loss z-scores, and elevated / severe loss-regime flags. The realized-losses lens on bank credit quality — the realized damage to delinquency's early warning. Companion to us-bank-credit-cycle-signals (volumes, delinquency) and us-sloos-bank-lending-standards-signals (standards). All rows normalized to country_code USA. Raw series: Board of Governors of the Federal Reserve System via FRED.
Quality
Attribution
Board of Governors of the Federal Reserve System via FRED; signals by Frontier Data Hub
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Observation date (FRED API field date; YYYY-MM-DD, quarterly). |
| country | string | |
| country_code | string | |
| series_id | string | CHARGEOFF_RE: charge-off rate on all real estate loans, all commercial banks (FRED CORALACBS); CHARGEOFF_CC: charge-off rate on credit card loans, all commercial banks (FRED CORCCACBS). Both from the Federal Reserve Board's charge-off and delinquency release. |
| series_label | string | Net percentage of domestic banks reporting charge-offs (annualized, net of recoveries) on the stated loan category (Federal Reserve Board definition). |
| value | float | Annualized net charge-off rate in percent (Federal Reserve Board). |
| momentum_3m | float | |
| yoy_change | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| loss_z_5y | float | |
| stress_flag | integer | |
| severe_flag | integer |
Sample rows
| date | country | country_code | series_id | series_label | value | momentum_3m | yoy_change | volatility_30d | anomaly_flag | forecast_1m | loss_z_5y | stress_flag | severe_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1985-01-01 | United States | USA | CHARGEOFF_CC | Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent) | 1.9 | — | — | — | 0 | — | — | 0 | 0 |
| 1985-04-01 | United States | USA | CHARGEOFF_CC | Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent) | 2.26 | 0.3599999999999999 | — | — | 0 | — | — | 0 | 0 |
| 1985-07-01 | United States | USA | CHARGEOFF_CC | Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent) | 2.67 | 0.41000000000000014 | — | — | 0 | — | — | 0 | 0 |
| 1985-10-01 | United States | USA | CHARGEOFF_CC | Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent) | 2.91 | 0.2400000000000002 | — | — | 0 | — | — | 0 | 0 |
| 1986-01-01 | United States | USA | CHARGEOFF_CC | Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent) | 3.13 | 0.21999999999999975 | 1.23 | — | 0 | — | — | 0 | 0 |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
Use with an LLM
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/loan_loss_signals/us_loan_loss_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/loan_loss_signals/us_loan_loss_signals").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/loan_loss_signals/us_loan_loss_signals
Tip: fetch /llms.txt for the full machine-readable catalog.