Research on the E-Commerce Credit Scoring Model Using the Gaussian Density Function

Abstract At present, it is simple to the electronic commerce credit scoring model, as a brush credit phenomenon in E-commerce has emerged. This phenomenon affects the judgment of consumers and hinders the rapid development of E-commerce. In this paper, that E-commerce credit evaluation model that uses a Gaussian density function is put forward by density test and the analysis for the anomalies of E-commerce credit rating, it can be fond out the abnormal point in credit scoring, these points were calculated by nonlinear credit scoring algorithm, thus it can effectively improve the current E-commerce credit score, and enhance the accuracy of E-commerce credit score. Keywords Abnormal Point, Credit Scoring, Density, E-commerce 1. Introduction With the development of information technology and social informatization, E-commerce (electronic commerce) has been rapidly developing. China’s E-commerce market turnover rate reached 3.5 trillion Yuan by June 2012 with a wear-on-year growth of 18.6% [1]. The online retail sales market turnover was is 511.9 billion Yuan, which was up 46.6% from the year before. The virtual commerce world is fast becoming a reality and in less than five years, it is estimated that nearly 70% of large business transactions will be conducted and signed on the Internet. E-commerce is completely focused on electronic data interchanges. In this new E-commerce environment, we can see that product decisions can be made based on online catalogues, which can be customized to the needs of the viewing client. Currently, these types of catalogues are customized based on recognizing a sign-on and secure password. As such, the purchaser has access to customized catalogues that reflect his/her needs and purchasing discounts [2,3]. However, the rapid development of E-commerce has triggered many problems. For example, E-commerce fraud is becoming more and more serious, brush poor commentary and good commentary have become a common means to change the credit score in the present E-commerce sites [4,5]. These factors can easily mislead consumers and increase the number of complaints from E-commerce sites.