Thesis (Ph.D.)--University of Melbourne, Dept. of Finance, Faculty of Economics and Commerce, 2002. / Typescript (photocopy). Includes bibliographical references.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 101-104). Also available in electronic version.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 84-90). Also available in electronic version.

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Thesis (Ph.D.)--University of Texas at Arlington, 2009.

Thesis (M. Phil.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 64-68) Also available in print.

M.Com. (Financial Management) / It has been argued that inadequate credit risk management practices and high levels of credit risk was the cause of the 2007 to 2009 global financial crisis, as well as the banking crises over the two past decades, including the 1997 East Asian crisis. As a result, banks have increasingly prioritised credit risk management to ensure acceptable levels of profitability and to keep them from collapsing. However, research on the relationship between credit risk management and profitability in banks in South Africa remains limited. Therefore, this study addressed the question of whether credit risk management has an effect on profitability in South Africa’s four major banks. A quantitative approach was used to establish the relationship between profitability, represented by return on equity (ROE), and credit risk management, represented by two variables, namely capital adequacy ratio (CAR) and the non-performing loans ratio (NPLR). Secondary data for the years 2002 to 2013 was analysed using panel regression and the study concludes that not only does credit risk management have an effect on profitability in South African banks, but that bank size, operating expenses and economic growth also affect the profitability of South African banks. These findings would enable the enhancement of profitability in South Africa through constantly improving credit risk management practices and policies, and by addressing other factors that can negatively affect profitability.

Práce pojednává o řízení rizik v leasingové společnosti. Popisuje proces hodnocení bonity klienta a faktory ovlivňující schvalování obchodních případů. Charakterizuje ratingový a scoringový model v konkrétní leasingové společnosti, hodnotí jejich nedostatky a navrhuje změny na jejich vylepšení. Obsahuje i praktický příklad komplexního hodnocení obchodního případu včetně posouzení bonity klienta prostřednictvím ratingového modelu a nástrojů finanční analýzy.

Abstract According to the latest statistical data from Ministry of Finance, it is found that domestic consuming loan is growing up continuously these years. Up to the end of September in 2000 the sum of this business is 3984.9 billion. It is equal to 34.1% among loan of native banks. Personal small-scale consumer credit is increasing at 18% rate per year from 148.6 billion in 1994 to 365.1 billion in the end of September in 2000. It is developed vigorously, and even to be the main profit for banks. This is because consumers have slowly changed their concepts about how to use their money. Another reason is that the banks are actively to provide small-scale consumer credit with easy formality. But its potential risk is becoming higher since depression in economy and unemployment are getting higher. ¡§How to do the credit estimation for your consumers; how to make the lost of breaking an appointment lower¡¨ is the most urgent for the banks who would like to have good performance in the field of consuming finance. This research takes 1764 consumers who have small-scale consumer credit from a specific bank as samples for analysis. We found 29 elements that will affect the payment from literature and credit estimation from other branches. After concluding 6 types of credit risk, 25 influent elements offered by sample bank are listed for the purpose of analysis. ¡§K-W independent check¡¨ and ¡§Spearman¡¦s rho related analysis¡¨ are used to gain 17 variables. They are interactive and remarkable for credit. The summarized introduction of this research is as follows. 1. Age is notable for payment. The risk between ages of 41 ~45 is higher than the average. Seniority around 7 ~10 years is also dangerous. The above appearance is figured out to be concerned about transition of economical environment such as depression in economy and unemployment. The thought ¡§ higher ages or seniority means lower risk¡¨ should be done some amendment. 2. Actual net income should be considered while estimating the credit. Higher income is not necessarily equal to lower risk. People with high income were easily to obtain more loans since they would have better payment capacity. It is observed from credit estimation of each bank. In fact income is unable to reflect payment capacity. Debt will be important reason to influence payment capacity. 3. Having real property doesn¡¦t mean having no risk. We could find that consumer¡¦s property usually took large percentage in credit estimation. Sometimes consumers would become dangerous since they had debt for real property. The banks had better to correct their illusion ¡¨land is wealth¡¨ as soon as possible. 4. More or less guarantees are not essential for credit risk. Simple and fast formality appeals to the popular while the banks are promoting small-scale consumer credit. In the past the banks believed that more guarantees could lower the risk. It is wrong and will be the block in developing business. The banks should focus on payment capacity as main accordance for credit estimation. Key words: Consumers loan; Credit risk management, Credit scoring system.

Banks today are the largest financial institutions around the world, with branches and subsidiaries throughout everyone’s life. However, commercial banks are facing risks when they are operating. Credit risk is one of the most significant risks that banks face, considering that granting credit is one of the main sources of income in commercial banks. Therefore, the management of the risk related to that credit affects the profitability of the banks. The aim of the research is to provide stakeholders with accurate information regarding the credit risk management of commercial banks with its impact on profitability.   The main purpose of the research is to investigate if there is a relationship between credit risk management and profitability of commercial banks in Europe. We also aim to investigate if the relationship is stable or fluctuating. In the research model, ROE and ROA are defined as proxies of profitability while NPLR and CAR are defined as proxies of credit risk management. The research collects data from the largest 47 commercial banks in Europe from 2007 to 2012 and formulates four hypotheses which are related to the research question. A series of statistical tests are performed in order to test if the relationship exists. Other statistical tests are performed to investigate if the relationship is stable or not.   The findings reveal that credit risk management does have positive effects on profitability of commercial banks. Between the two proxies of credit risk management, NPLR has a significant effect on the both ROE and ROA while CAR has an insignificant effect on both ROE and ROA. However, from 2007 to 2012, the relationships between all the proxies are not stable but fluctuating.