The Decision Tree In The Application Of Small Farmer Credit Risk Assessment Research

For a long time, rural credit cooperatives plays an important role in rural financial service. It has made outstanding contribution to the agricultural production, increasing farmers' income and rural economic development. Farmers' micro-finance as a kind of special service to farmers' financial products, rural credit cooperatives has a pivotal position in the pursuit of efficiency and performance of social responsibilities. At the same time, as an effective tool of rural poverty reduction, farmers' micro-finance has received the widespread attention from all walks of life. Farmers' micro-credit can effectively relieve farmers' loans difficult problems, and actively promote the agricultural i ncome and support the development of rural economy. However, with the increasing number of farmers' micro-credit business, and some of its own characteristics, made rural credit cooperatives facing higher than general loan credit risk in the process of the issue of peasant household micro-finance, the loan risk conditions directly affect the long-term survival and development of the credit union. In recent years, according to the national policy, the rural credit cooperatives need to be reformed to a rural commercial bank. Due to the main customers of the rural credit cooperatives are the friends of farmers, the loan risk is greater than other banks, so it put forward higher request for the management of the loan.This paper first introduces the development of the small peasant household loans at home and abroad, and describes the present situation of the rural credit cooperatives credit management and some of the challenges faced. And then to introduce the basic concepts of data mining, and describes the commonly used data mining methods and key technologies in detail, and compares their features analysis, and how to practice in mining. Then take the Heng Nan county of Hunan province rural credit cooperatives as the background, it describes the basic pattern and some farmers' micro-credit risk in detail, and analyzes the characteristics of these risks and assessment. At the same time, it introduces how to apply data mining technology to credit risk management methods and steps of rural credit cooperatives, mai nly describes the decision tree C4.5 algorithm is compared with other data mining methods for rural credit cooperatives micro-finance customer credit evaluation and loan risk management advantages, basic principle, algorithm design and the decision tree analysis process and the actual build process. Then according to the characteristics of the rural credit cooperatives credit business, combining with data mining knowledge of relevant data found that advantage, this paper proposes a model suitable for rural credit cooperatives credit management system and data mining system architecture design, and implements a simple design of rural credit cooperatives small loans risk intelligent auxiliary analysis system. Finally, in this article, summarizes the work, it points out some limitations of the research, and make the outlook for the future research work. The results of the study provide a reference for the small peasant household loans management after the reform of the credit cooperatives into rural commercial bank.