National student loans credit risk assessment based on GABP algorithm of neural network

Policy of national student loans accelerates the reform of higher education in China and the process of market mechanism of talents training in a very great degree, and provides the important guarantee for the poor college students. However, at present, high default rate makes commercial bank which provides student loans bear the risk of bad debt, and affects the policy of national student loan to develop smoothly to a certain extent. This paper uses the improved GABP algorithm of neural network to construct national student loans credit risk assessment model by which identify credit risk level. This paper will effectively reduce national student loans credit risk and promote the healthy development policy of national student loan.