Research into Bank Loan Risk Based on UDM and Self-adaptive RBF Neural Network

At present, the application of neural network technology in our country's bank loan risk evaluation is very limited. The reason lies in the difficulty to find high quality training samples for neural network self-learning. Therefore, we adopt uniform design method (UDM) to design representative, uniformity and large-scale samples. And then we use those samples to train the self-adaptive radial basis function neural network (RBFNN) which is applied to carry out the bank loan risk evaluation. The result of the experiment shows that the generalization ability of self-adaptive RBFNN combined with UDM is far better than that of traditional RBFNN based on Monte-Carlo method. The self-adaptive RBFNN combined with UDM not only realizes the self-adaptive ability and non-linear approaching ability, but also conquers the performance limitations of traditional RBFNN. And also it avoids the subjectivity and uncertainty of traditional evaluation.