Sumitomo Mitsui Financial Group, Inc. Credit Rating

BOSTON (AI Credit Rating Terminal) Wed Jul 22 2020 11:49:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Sumitomo Mitsui Financial Group, Inc. because the steps to carry out a bail-in of that type of liability would involve such high operational complexities that would make its bail-in unlikely in a reasonable time. We use econometric methods for period (n+30) simulate with Relative Strength Index (RSI) Wilcoxon Sign-Rank Test. Reference code is: 1920. Beta DRL value REG 21 Rational Demand Factor LD 4581.7548. If, for example, a facility matured in 18 months, we could include the borrowing availability as a source of liquidity in year one, but exclude the amount in year two under the exceptional and strong descriptors (as well as include any drawn portions as debt maturities under uses of liquidity). This is because we do not assume an extension of bank lines--regardless of the company's perceived credit strength or issuer credit rating. For instance, whether the issuer credit rating on the company is speculative grade or investment grade, we do not assume bank lines will be extended beyond the current stated maturity. 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.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 Sumitomo Mitsui Financial Group, Inc. as below:

Credit Ratings for Sumitomo Mitsui Financial Group, Inc. as of 22 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba2B1
Semantic Signals5357
Financial Signals9087
Risk Signals7938
Substantial Risks7769
Speculative Signals4237

*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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