With the intense competition and increasing globalization in the financial markets, bank management must develop customer-oriented strategies in order to compete successfully in the competitive retail banking environment. The longer a bank can retain a customer, the greater revenue and cost savings from that customer. However, customers are also more prone to changing their banking behaviour when they can purchase nearly identical financial products provided by the retail banks. In order to stay competitive, bank managers need to understand the factors that influence and determine consumer’s bank switching behaviour. With China's accession to the World Trade Organization (WTO), their financial services market was liberalized and deregulated. As a result, customers have a greater choice between domestic and foreign banks. Furthermore, the emergence of the internet allows customers to access financial products without limitation, and increases the Chinese retail banks’ ability to prevent customers’ switching banks. This study identifies and analyses the factors that influence bank customers’ switching behaviour in the Chinese retail banking industry. The findings reveal that Price, Reputation, Service Quality, Effective Advertising, Involuntary Switching, Distance, and Switching Costs have an impact on customers’ bank switching behaviour. The results also reveal that the Young Age and High Income Groups are more likely to switch banks. In general, the results of this research allow service marketers and practitioners to develop and implement services marketing strategies to decrease customer defection rates, and in turn, increase bank profits. Furthermore, this research provides useful information for future researchers who study switching-behaviour in the banking industry.

This thesis provides a comprehensive ranking of 11 Czech statistical and 4 foreign credit scoring models. The ranking is based on the predictive performance of individual models, as measured by the area under curve, evaluated on a randomly sampled set of 250 training and validation samples. After establishing a baseline comparison, 3 avenues of estimation setup optimization are explored, namely missing value treatment, estimation method and the use of additional non-financial variables. After being optimized, the models are once again ranked based on their predictive performance. Statistical inference is drawn using ANOVA and the Friedman test, along with the corresponding Tukey and Nemeyi pos-hoc tests. In their baseline form, the Czech credit scoring models are found to be outperformed by the foreign benchmark model. Treating the missing values by OLS imputation and estimating the models by probit, significantly is found to significantly improve their predictive performance. In their optimized form, the difference in predictive performance between Czech and foreign credit scoring model is found to be only marginal. JEL Classification G28, G32, G33, G38 Keywords credit scoring, multiple discriminant analysis, logit analysis, probit analysis Author's e-mail 71247263@fsv.cuni.cz Supervisor's e-mail...

This diploma thesis is focused on the evaluation of the efficiency of selected bankruptcy models in the Czech Republic. In the theoretical part the basic terminology and methodology of bankruptcy models creation are introduced. In addition are mentioned, model constraints, an overview of the indicators used, and information about model accuracy. This part also presents analyzed models and methods of assessing the reliability of bankruptcy models. In the practical part, the reliability of selected bankruptcy models is evaluated and a new bankruptcy model is built.