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U.S. Bank National Association Credit Rating & Financial Statements Analysis

BOSTON (AI Credit Rating Terminal) Wed Jun 16 2021 02:00:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Financial Statements Overview & Credit Rating Rationales


We rerated U.S. Bank National Association because In addition to the risk weight based on revenues by business line, we apply a risk weight of 6.25% to cash and money market . (We use econometric methods for period (n+1) simulate with Running Moving Average (RMA) Sign Test). When determining the cash to be included under sources (A), we use cash that will be available to cover monetary outflows. As a result, we may make haircuts to account for cash trapped overseas (for example, haircut for taxes payable upon repatriation of cash held abroad), apply a discount to lower-quality marketable securities, and exclude restricted cash held for specific purposes. Credit Rating AI Process rely on primary sources of information: Sec Filings, Financial Statements, Credit Ratings, Semantic Signals. Take a look at Machine Learning section for Financial Deep Reinforcement Learning.

Risk Heat Map for U.S. Bank National Association as of 16 Jun 2021


Oscillators are used for generating credit risk signals by using the semantic and financial signals. The value of the oscillators indicate the strength of trend. Using the correlation matrices, the credit rating risk map for U.S. Bank National Association as below:

Credit Ratings for U.S. Bank National Association as of 16 Jun 2021


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B1B2
Semantic Signals8381
Financial Signals6880
Risk Signals7031
Substantial Risks4945
Speculative Signals4030

*Machine Learning utilizes multiple learning algorithms to obtain better predictive powers. In our research, we utilize machine learning to combine the results from the Neural Network and Support Vector Machines.
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Disclaimers: AC Investment Inc. currently does not act as an equities executing broker, credit rating agency or route orders containing equities securities. In our Machine Learning experiment, we focus on an approach known as Decision making using game theory. We apply principles from game theory to model the relationships between rating actions, news, market signals and decision making.The rating information provided is for informational, non-commercial purposes only, does not constitute investment advice and is subject to conditions available in our Legal Disclaimer. Usage as a credit rating or as a benchmark is not permitted.

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