Credit risk management in rural commercial banks in China

• Main Author: Wang, Yang
• Other Authors: Gao, Simon
• Published: Edinburgh Napier University 2013
• Subjects: 332.1; HG Finance
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.601309

Credit risk is one of the most general risks that exist in the financial market and a major risk faced by financial institutions. Credit risk management (CRM) is to identify, measure, monitor, and control risk arising from the possibility of default in loan repayments. The primary objective of CRM of rural commercial banks (RCBs) is to maintain risk within acceptable parameters and satisfy the regulatory requirements. CRM has long been the focus of governments, regulatory authorities and financial institutions. This thesis examines the importance of CRM for RCBs, which has been overlooked in the literature, and attempts to develop a CRM framework for RCBs. It has four specific research objectives: 1) to discuss the differences between RCBs and city based-commercial banks; 2) to examine the importance of CRM for RCBs and identify the approaches available for banks to manage credit risks; 3) to identify the key factors that have influenced the credit evaluation and assessment, as well as credit risk control in the context of China's RCBs; and 4) to propose a practicable CRM framework that suits the characteristics of Chinese RCBs. This study adopts qualitative analysis and case study approaches to identify key factors contributing to the failure of RCBs' customers, resulting in loan defaults and banks' credit risk. The quantitative-based CRM tools available for large financial institutions do not meet the requirements of RCBs because the main customers of RCBs are small and medium-sized enterprises (SMEs) and farming households and there is a lack of financial data and credit rating relating to these customers. In addition to normal risks faced by financial institutions, RCBs in China are also exposed to risks specifically to rural commercial banking business and in particular, farming-related loans and services. This study proposes a CRM framework for RCBs in China. The framework is based on the identification of business failures of RCBs' customers and factors contributing to the failures of SMEs and farming households. The framework is divided into five steps. The first step is to distinguish business failure and closure. The second step is to identify factors contributing to the failure of customers, which should be considered from environmental, operational, financial and guanxi aspects. The third step is to use PCA to identify principal factors. The fourth step is to design a credit risk analysis model with an analysis of these principal factors. The final step is to use the credit risk analysis model to manage credit risks of their portfolios and individual loans provided to SMEs and farming households. The CRM framework has been confirmed by practitioners through interviews conducted in the case bank. Interviews raise a number of issues relating to the development of a CRM model and assessment of credit risk of SMEs in China. The case study through an analysis of documents of the case bank reveals the importance of CRM and organisational structure in risk management and CRM. The case study presents evidence of lacking of practical methods in managing credit risk by RCBs in China. The proposed framework expects to address the problem. This study has made several contributions to the literature that studies CRM in financial institutions in general and RCBs in particular. This study critically identifies the current lack of studies specifically addressing the RCBs' CRM, and proposes a CRM framework for RCBs. The framework considers financial and non-financial variables to analyse SMEs and farming household for which financial information is very limited. Using nonfinancial variables along with financial variables as predictors of business failure significantly improves credit analysis quality and accuracy. Also, this study recognises guanxi as risk potentials affecting the business of SMEs and farming households and includes guanxi risks in the framework. The consideration of guanxi in credit risk analysis fits well with China's business environment.