| Abstract: |
| As a supplement to the financing of small and medium-sized enterprises, bank microfinance companies are non-bank financial institutions, it has played an active role in maintaining the stability of financial markets. However, in the course of the operation of microfinance companies, due to the lack of careful management and risk control, the problem of risk management has become increasingly prominent. The purpose of this paper is to study the microfinance risk based on polygenic artificial neural network, it provides theory and practice application for risk management of micro-credit enterprises. Taking the risk management of China`s microfinance companies as the
research object, on the basis of previous studies, this paper analyses the risks of bank microfinance companies. Secondly, the basic theory of neural network model and its transformation function are introduced, and the earning method of neural network. At the same time, the learning algorithm of neural network and its improved algorithm are mainly introduced. It lays a theoretical foundation for the follow-up empirical research.
Then, through the empirical study of data-based risk assessment of micro-credit of farmers, the sample data are divided into training samples and test samples for comparison. Then, we use MATLAB software to establish a neural network model for farmers` micro-credit risk assessment. Finally, in order to make the neural network
model of farmers` credit risk assessment better popularize and apply, and effectively reduce the credit risk of farmers` micro-credit. The corresponding policy suggestions are put forward, which proves the validity and applicability of the neural network in the field of farmers` micro-credit risk assessment. It provides a good basis for rural credit cooperatives to identify the credit risk of farmers. |
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