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In this thesis I examine the opportunities and challenges faced by the community banks in China. Rooted in the local communities, community banks generally focus on serving the local residents, farmers, and micro and small business enterprises (MSBE) through relationship building. Although community banks tend to be small relative to the other financial institutions, their unique market positions and business strategies have helped them to survive the competition and secure some market shares. Thus, it is important to understand the business strategies of community banks and to explore their future business opportunities and challenges.
I first provide a brief overview about the importance of local communities, community economy, and community banking, on the basis of an analysis about mismatch in the demand and supply of community financial services due to information asymmetry. Next, I review and analyze how commercial banks have utilized different types of information in their operations. I classify the information used by commercial banks into different categories and discuss their importance to the operations of commercial banks. After that, I conduct a case analysis to illustrate the role of non-financial information in the development of community banks’ business strategy. I conclude this thesis with a discussion of how community banks can better utilize data analysis to develop their core competencies in the era of “Big Data”.
I first provide a brief overview about the importance of local communities, community economy, and community banking, on the basis of an analysis about mismatch in the demand and supply of community financial services due to information asymmetry. Next, I review and analyze how commercial banks have utilized different types of information in their operations. I classify the information used by commercial banks into different categories and discuss their importance to the operations of commercial banks. After that, I conduct a case analysis to illustrate the role of non-financial information in the development of community banks’ business strategy. I conclude this thesis with a discussion of how community banks can better utilize data analysis to develop their core competencies in the era of “Big Data”.
ContributorsHou, Funing (Author) / Li, Feng (Thesis advisor) / Wang, Jiang (Thesis advisor) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2015
Description
During the past decade, the Chinese bond market has been rapidly developing. The percentage of bond to total social funding is constantly increasing. The structure and behavior of investors are crucial to the construction of China’s bond market. Due to specific credit risks, bond market regulation usually involves in rules to control investor adequancy. It is heatedly discussed among academia and regulators about whether individual investors are adequate to directly participate in bond trading. This paper focuses on the comparison between individual and institutional bond investors, especially their returns and risks. Based on the comparison, this paper provides constructive suggestions for China’s bond market development and the bond market investor structure.
ContributorsLiu, Shaotong (Author) / Gu, Bin (Thesis advisor) / Zhu, Ning (Thesis advisor) / Yan, Hong (Committee member) / Arizona State University (Publisher)
Created2016
Description
Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis – the first focuses on fraud detection at the store level, and the second focuses on fraud detection at the merchant level by aggregating store level data to the merchant level for merchants with multiple stores. My purpose is to put the model into business operations, helping to identify fraudulent merchants at the time of transactions and thus mitigate the risk exposure of the payment acquiring businesses. The model developed in this study is distinct from existing fraud detection models in three important aspects. First, it predicts the probability of fraud at the merchant level, as opposed to at the transaction level or by the cardholders. Second, it is developed by applying machine learning algorithms and logistical regressions to all the transaction level and merchant level variables collected from real business operations, rather than relying on the experiences and analytical abilities of business experts as in the development of traditional expert systems. Third, instead of using a small sample, I develop and test the model using a huge sample that consists of over 600,000 merchants and 10 million transactions per month. I conclude this study with a discussion of the model’s possible applications in practice as well as its implications for future research.
ContributorsZhou, Ye (Author) / Chen, Hong (Thesis advisor) / Gu, Bin (Thesis advisor) / Chao, Xiuli (Committee member) / Arizona State University (Publisher)
Created2015