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In response to the rapid development of the global economy and FinTech innovation, Taiwan's government has continuously promoted digital transformation policies for the financial industry and encouraged the industry to strengthen infrastructure constructions and upgrade innovative financial services and patents. Novel ideas are proposed to create a new situation and make banks invincible but it is questionable if such innovations necessarily bring competitiveness or business performance. To answer this question, we analyzed market competition using the PR test and data envelopment analysis (DEA) to study the operational efficiency of banks. K-means were also used to segment banks into three groups, Leader, Chaser, and Laggard. The data included financial (business performance) and patent data (innovation) of Taiwan's banks from 2013 to 2020. The research results revealed that the market competition in the Leader group was the most intense. Quasi-public banks were relatively inefficient in creating revenue, while private banks were more efficient. The Chaser group showed the most apparent changes in operational efficiency from 2013 to 2020. The banks in the Laggard group needed to strengthen the relative efficiency of revenue, and financial innovation needed to be individualized in this competitive market group. [ABSTRACT FROM AUTHOR]

Abstract: By providing credit risk information, credit rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries. In this paper, we propose an automatic classification model for issuer credit ratings, a type of fundamental credit rating information, by applying the support vector machine (SVM) method. This is a novel classification algorithm that is famous for dealing with high dimension classifications. We also use three new variables: stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification. Previous research has seldom considered these variables. The data period of the input variables used in this study covers three years, while most previous research has only considered one year. We compare our SVM model with the back propagation neural network (BP), a well-known credit rating classification method. Our experiment results show that the SVM classification model performs better than the BP model. The accuracy rate (84.62%) is also higher than previous research. [Copyright &y& Elsevier]