Programmatic Guide to US Bank Credit Ratings

For structured-finance analysts and data engineers, US bank credit ratings are more than just letter grades; they are critical inputs for counterparty risk models, deal surveillance, and programmatic analysis. Understanding their data lineage—where they originate, how they are derived, and how they connect to specific legal entities—is essential for building explainable and reproducible financial workflows. This guide deconstructs bank ratings from a data-centric perspective, linking them to verifiable sources like EDGAR filings and showing how to integrate them into automated risk monitoring. You can visualize and cite much of this connected data using platforms like Dealcharts.

Market Context: Why Bank Ratings Matter in Structured Finance

In the ABS and CMBS markets, bank credit ratings are a direct proxy for counterparty risk. Banks act as trustees, servicers, and liquidity providers—the operational backbone of a structured deal. A servicer's financial stability, signaled by its rating, impacts its ability to manage loan collections and advance payments, especially during market stress. These ratings are not static; they are heavily influenced by systemic economic forces and regulatory shifts.

The current environment of elevated interest rates and tightening credit conditions has put significant pressure on bank balance sheets. As the Federal Reserve combatted inflation, banks faced a dual challenge: unrealized losses on their fixed-rate securities portfolios and increased funding costs as depositors moved cash to higher-yielding assets. This dynamic, which triggered market turmoil in 2023, prompted agencies like Moody's to downgrade the outlook for the entire US banking system. For analysts, the key challenge is to quantify how these macro headwinds translate into specific risks for the bank counterparties embedded in their deals, connecting today's pressures to historical performance data like that seen in CMBS vintage data from 2008.

The Data & Technical Angle for US Bank Credit Ratings

Verifiable credit ratings data originates from three primary sources, each with distinct implications for data engineers and quants.

• Direct Agency Feeds: The gold standard for timeliness and granularity. Moody's, S&P Global Ratings, and Fitch Ratings offer commercial APIs and data feeds. Ingesting this data requires building pipelines to handle multiple schemas and licensing costs, but provides the highest-quality, structured data for real-time surveillance.
• Third-Party Aggregators: Platforms like Bloomberg and Refinitiv ingest and normalize data from multiple agencies, simplifying the ingestion process. The trade-off is often a slight data latency and less granular metadata compared to direct feeds.
• Public Filings (SEC EDGAR): Banks disclose ratings in regulatory filings like 10-K and 8-K reports. This is the most cost-effective source but presents the greatest technical challenge, requiring robust parsing logic to extract ratings from unstructured text and link them to the correct reporting period.

The most critical technical challenge is entity linking. A rating for a bank holding company (e.g., "U.S. Bancorp") is not the same as a rating for its primary operating subsidiary (e.g., "U.S. Bank National Association"). A failure to distinguish between these legal entities can render a risk model invalid. Robust pipelines must map entity names to standardized identifiers like a Legal Entity Identifier (LEI) or a Central Index Key (CIK). You can explore these entity structures for major issuers via the US bank shelf registrations on Dealcharts.

Example Workflow: Programmatic Verification of Ratings

A reproducible workflow must connect a rating to a unique, verifiable entity identifier. This establishes a clear data lineage: source (API/filing) → transform (entity linking) → insight (risk metric). The following Python snippet demonstrates the core logic of merging ratings data with standardized entity identifiers to create a verifiable dataset.

import pandas as pd

# Hypothetical bank entity data with CIK and LEI
bank_entities = {
    'BankHoldingCompany': ['U.S. Bancorp', 'JPMorgan Chase & Co.', 'Bank of America Corp'],
    'CIK': ['0000036104', '0000019617', '0000070858'],
    'LEI': ['L3I9ZG2KFGXZ61S8PG26', '7H6GLXDRUGQFU57RNE97', 'G3I56F72R7Y4S2W64475']
}
entities_df = pd.DataFrame(bank_entities)

# Hypothetical ratings data fetched from an API or database
ratings_data = {
    'RatedEntity': ['U.S. Bancorp', 'JPMorgan Chase & Co.', 'Bank of America Corp'],
    'SP_Rating': ['A+', 'A-', 'A-'],
    'Moodys_Rating': ['A1', 'A2', 'A1'],
    'RatingDate': ['2024-10-26', '2024-10-25', '2024-10-22']
}
ratings_df = pd.DataFrame(ratings_data)

# Merge datasets on a common key to link ratings to standardized identifiers
verified_ratings_df = pd.merge(entities_df, ratings_df, 
                               left_on='BankHoldingCompany', 
                               right_on='RatedEntity')

print("--- Verifiable Bank Ratings Data ---")
# The output is a structured dataset with clear lineage for each rating
print(verified_ratings_df[['CIK', 'LEI', 'BankHoldingCompany', 'SP_Rating', 'Moodys_Rating', 'RatingDate']])

This process transforms raw data points into a structured, model-ready format where each rating is unambiguously tied to a legal entity. This explainable pipeline is the foundation for any credible risk surveillance system.

Implications for Modeling and Risk Monitoring

Integrating ratings data with this level of structured context fundamentally improves modeling and risk monitoring. When ratings are linked to deal-level data, such as the specific CMBS transactions a bank services, analysts can build "model-in-context" surveillance. For example, a downgrade of a servicer's rating can trigger an automated alert, prompting an analyst to review the pooling and servicing agreement (PSA) for contractual covenants tied to that rating. These covenants might include servicer replacement triggers or collateral posting requirements.

This approach creates an explainable pipeline where a change in a macro indicator (a bank rating) can be traced directly to its micro impact (a specific deal covenant). This level of context is also invaluable for training large language models (LLMs) on financial data. An LLM with access to this connected graph can reason about the second-order effects of a rating change, moving beyond simple data retrieval to generate genuine risk insights. This is the core principle of a CMD+RVL context engine: enriching data with verifiable relationships to enable more sophisticated, automated analysis.

How Dealcharts Helps

Connecting disparate datasets—filings, deal documents, servicer reports, and counterparty ratings—is a significant data engineering challenge. Dealcharts handles this heavy lifting by pre-building these connections into a verifiable context graph. It links filings, deals, shelves, tranches, and counterparties so analysts can publish and share verified charts without rebuilding data pipelines. This allows quants and data engineers to move directly from data to insight, focusing on analysis rather than data plumbing.

Conclusion

Effective use of US bank credit ratings in a professional context demands a programmatic, data-centric approach. By focusing on data lineage, precise entity linking, and integration with deal-level context, analysts and engineers can build explainable pipelines for risk modeling and surveillance. This transforms static ratings into dynamic inputs, enabling more sophisticated and reproducible financial analytics—the foundation of the CMD+RVL framework.

Comparing the Major Credit Rating Agency Scales

To make sense of it all, it helps to have a quick reference for how the different agencies stack up. While they all aim to measure the same thing—credit risk—their letter grades aren't perfectly interchangeable. Each has its own slight variation.

Here's a quick cheat sheet to translate between the scales for long-term issuer credit ratings.

This table covers the main tiers, from the highest "Prime" ratings down to "In Default." The first four tiers (down to Baa3/BBB-) are generally considered investment grade, while anything below that falls into the speculative-grade or "junk" category. Understanding where a bank falls on this spectrum is the first step in assessing its risk profile.

Issuer vs. Issue Ratings: What's the Difference?

Think of an issuer credit rating (ICR) as the bank's overall report card. It's the agency's take on the bank's general ability to pay its bills. A single grade for the whole institution.

An issue-specific rating, on the other hand, zooms in on a particular piece of paper—say, a specific bond the bank sold. This rating gets granular, looking at things like seniority or collateral. That's why a specific bond can have a different rating than the bank itself.

Article created using Outrank