한국해양대학교

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A Study of Competitiveness of Commercial Banks in East Asia

DC Field Value Language
dc.contributor.advisor Kim Jae-bong -
dc.contributor.author GUO Ruijin -
dc.date.accessioned 2022-06-23T08:57:43Z -
dc.date.available 2022-06-23T08:57:43Z -
dc.date.created 20220308093443 -
dc.date.issued 2022 -
dc.identifier.uri http://repository.kmou.ac.kr/handle/2014.oak/12842 -
dc.identifier.uri http://kmou.dcollection.net/common/orgView/200000603134 -
dc.description.abstract The 4th industrial revolution is changing modern society dramatically from the way people used to live and the mode businesses used to operate. With the trend of digitalization and globalization, the conventional economic activities and financial systems are also deeply reshaped. The banking industry, playing a dominant role in financial systems, is also transformed from “the business of saving money” to “the business of money” and currently “the business of customers”. Nowadays, the bank industry faces two major challenges. On the one hand, brought by digitalization, the rapidly growing business volume challenges the bank’s customer risk estimation. In addition, the strengthened regulatory requirements also raised expectation for higher efficiency and accuracy. On the other hand, brought by globalization, banks also faced the challenge of obtaining a superior position from the local, foreign, and Fintech competitors. Therefore, this thesis focuses on strengthening the competitiveness of commercial banks by decreasing the risk brought by customers and increasing the brand value of banks. Specifically, this thesis addresses the following three main contributions: First of all, an accurate and efficient customer risk-level classifier is developed with neural network at the account opening service, which provides a first-gated risk assessment for customers. Experimental results showed that the proposed method achieved 96.7% accuracy, which surpasses existing methods. Such an automatic and labor-saving risk-level classifier is a crucial step to efficiently mitigate banks’ potential risks and losses, which also benefits the brand of banks. Secondly, to generate more connections with new customers, a bank-oriented study is developed to systematically investigate the impact of bank brand value from macro, meso, and micro levels with multi-variables regression analysis. The importance of the three levels of factors is numerically estimated respectively. Micro-level is found to play the most influential role. Consequently, a further investigation between the micro-level variables and bank brand value is explored. The findings guide the bank managers to optimize resource allocation. By grasping the core of the brand value development, banks can attract more customers so that obtain a superior position in the market. Thirdly, a comparison study of the top banks in China and Korea is conducted. As the key representatives of emerging economies and developed economies respectively from East Asia, both China and Korea show increased importance and influence on the global stage. The findings of this study show commonalities and heterogeneities of factors that contribute to a bank’s brand value in both markets, suggesting that customized strategy should be applied to fit the different economic realities of bank’s-based country. -
dc.description.tableofcontents Chapter 1 Introduction 1 1.1 Background 1 1.2 Purpose of Research 4 1.3 Methodologies and Key Contributions 8 1.4 Organization of the Thesis 9 Chapter 2 The First Gate Customer Risk Assessment 12 2.1 Risk Assessment in the Retail Banking Business 12 2.1.1 External environment: digital economy and finance 12 2.1.2 Changes in the environment of banking business 15 2.1.3 Challenges to the banking business 16 2.2 Limitations of Existing Customer Risk Assessment Frameworks 18 2.3 First Gate Risk Assessment: Procedures in Account Opening Service 25 2.4 Automatic Customer Risk-level Classifier with the Neural Network at the Account Opening Service 30 2.4.1 Data collection and preprocessing 33 2.4.2 Feature extraction 35 2.4.3 2-staged NN-based customer’s risk classification model 45 2.5 Experimental Results and Analysis 49 2.5.1 Overall performance 52 2.5.2 Comparison with other machine learning algorithms 52 Chapter 3 Strengthen the Brand in the Banking Business 55 3.1 The Role of Brand in Banking Business 55 3.2 Customer-oriented Brand Studies in the Banking Business 58 3.3 Bank-oriented Brand Studies 60 3.4 Bank Core Competence Model 62 3.5 Importance Analysis with Multi-variable Linear Regression 72 3.5.1 Data collection 72 3.5.2 Data preprocessing: principal component analysis 74 3.5.3 Multivariable regression analysis 78 3.5.4 Multicollinearity problem: stepwise regression 79 3.6 Results and Analysis 80 3.6.1 PCA based factor analysis in three levels 80 3.6.2 Analysis of the benchmark regression 83 3.6.3 Stepwise regression with Micro level variables 85 3.6.4 Comparison between banks in Korea and China 88 Chapter 4 Discussion and Implication 93 4.1 Implications of Bank’s Risk Assessment 93 4.2 Implications of Bank’s Brand Value 95 4.3 The Implication of Brand Value Improvement with Strengthened Risk Control are Inseparable in Bank Business 98 Chapter 5 Conclusion 101 -
dc.format.extent 137 -
dc.language eng -
dc.publisher 한국해양대학교 대학원 -
dc.rights 한국해양대학교 논문은 저작권에 의해 보호받습니다. -
dc.title A Study of Competitiveness of Commercial Banks in East Asia -
dc.title.alternative 동아시아 상업은행의 경쟁력에 관한 연구 - 리스크 및 브랜드를 중심으로 - -
dc.type Dissertation -
dc.date.awarded 2022. 2 -
dc.embargo.liftdate 2022-03-08 -
dc.contributor.alternativeName GUO Ruijin -
dc.contributor.department 대학원 무역학과 -
dc.contributor.affiliation 한국해양대학교 대학원 무역학과 -
dc.description.degree Doctor -
dc.identifier.bibliographicCitation [1]GUO Ruijin, “A Study of Competitiveness of Commercial Banks in East Asia,” 한국해양대학교 대학원, 2022. -
dc.subject.keyword Commercial Bank -
dc.subject.keyword Risk Assessment -
dc.subject.keyword Brand Value -
dc.subject.keyword Competitiveness -
dc.subject.keyword Comparison Analysis -
dc.title.partName Focusing on Risk and Brand Perspective -
dc.contributor.specialty Economics -
dc.identifier.holdings 000000001979▲200000002763▲200000603134▲ -
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