Application Of The Algorithm Based On The PSO And Improved SVDD For The Personal Credit Rating

In recent years, with the fast development of China’s economy, the credit consumption is increasing rapidly, and the size of a variety of individual consumption credit such as housing mortgage, credit card consumption, car loans, and so on are appearing the trends of rapid expansion. All commercial banks begin to take developing individual consumption credit as an important part of their future development strategies. But for the present, the risk management level for individual consumption credit of domestic commercial banks is still relatively low, the management method is relatively backward, and there still lacks a set of scientific personal credit evaluation system. The accuracy rate for personal credit evaluation will directly affect profit of credit institution, which demands credit institution possess a scientific and perfect credit management system, thus the study on personal credit evaluation is of high significance.Besides, considering that under the big data environment, the size of data is increasing rapidly. In the period with massive data resources, the credit rating industry are faced with both new opportunities and new challenges.In response to the problem of personal credit evaluation under the era of big data, this paper undertakes exploration on data analysis and integration, and attempts to reduce the dimensionality of the data utilizing related technology. Thus achieving the purpose of decreasing computational costs and further increasing the accuracy of classification, this paper proposes a hybrid credit rating method which is established based on particle swarm optimization algorithm and the improved SVDD algorithm. Firstly, the paper preprocesses on the credit data, then it uses PSO to conduct simultaneously the feature extraction of sample data and the parameter optimization of SVDD algorithm, and forms the final hybrid credit evaluation model combing with the improved SVDD algorithm. Finally, this paper applies the established model on two real credit data sets, and according to the empirical results, it can be found that the model established in this paper can improve the accuracy of classification compared with several other credit evaluation methods.