Empirical innovative model for predicting national banks' financial failure with artificial intelligence subset data analysis in the U.S.

The major objective of this innovative research study was to explore the degree to which national systemically important banks’ value investment, in terms of how the price to earnings ratio impacts their return on equity. We used statistical modeling and the artificial intelligence model to find hidden patterns in the input data from a list of systemically important banks. The principle finding of this research is that the financial factor that helps the financial institution, causes a cascading failure with the impact on the World economics. These findings of the new ratios formulas can contribute to improving our understanding of how systemically important banks can predict financial modern risk, using the new feature of artificial intelligence to build an early warning system in real-time. In this research article, we develop an innovative predicting risk model to measure possible contagion bank risk for Systemically Important Financial Institutions, which is defined as the risk that an initial bank failure may spill over to the rest of the banking industry and cause further bank failures.