US loan-loss signals (charge-off rates, stress regimes)

Name: US loan-loss signals (charge-off rates, stress regimes)
Creator: US Loan-Loss Signals (derived)
License: https://fred.stlouisfed.org/legal
Keywords: banks, charge-offs, loan-losses, credit-quality, credit-cards, real-estate-loans, credit-cycle, financial-stability, momentum, anomaly-detection, forecasting, signals, fred

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.

Source: US Loan-Loss Signals (derived)332 lignesMis à jour: 22/09/2026

bankscharge-offsloan-lossescredit-qualitycredit-cardsreal-estate-loanscredit-cyclefinancial-stabilitymomentumanomaly-detectionforecastingsignalsfred

Qualité

99.1

Attribution

Board of Governors of the Federal Reserve System via FRED; signals by Frontier Data Hub

Schéma

Colonne	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

Exemple de lignes

date	country	country_code	series_id	series_label	value	momentum_3m	yoy_change	volatility_30d	forecast_1m	loss_z_5y
1985-01-01	United States	USA	CHARGEOFF_CC	Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent)	1.9	—	—	—	—	—
1985-04-01	United States	USA	CHARGEOFF_CC	Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent)	2.26	0.3599999999999999	—	—	—	—
1985-07-01	United States	USA	CHARGEOFF_CC	Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent)	2.67	0.41000000000000014	—	—	—	—
1985-10-01	United States	USA	CHARGEOFF_CC	Charge-Off Rate: Credit Card Loans, All Commercial Banks (quarterly, percent)	2.91	0.2400000000000002	—	—	—	—
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	—	—	—

Télécharger un échantillon

Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

Télécharger l'exemple CSV Télécharger l'exemple JSON

Utiliser avec un LLM

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (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)

Point d’accès API: https://datazimuts.com/v1/datasets/loan_loss_signals/us_loan_loss_signals

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.