Credit Scoring with AHP and Fuzzy Comprehensive Evaluation Based on Behavioural Data from Weibo Platform - Jia Yu; Jianrong Yao; Yuangao Chen (Tehnički Vjesnik, 2019)
It is increasingly necessary to evaluate the customers' credit. In the era of big data, Information on the Internet is commonly used to judge the credit worthiness of customers. Some users' credit information is incomplete or unavailable, so credit managers cannot judge the true credit situation of these users. However, with the support of social data especially behavioural data and credit evaluation system, this problem can be effectively solved. This study used Weibo to obtain the behavioural data of Chinese users for credit evaluation. Two methods are used to calculate the credit scores of Weibo users, which are the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods. By analysing social processes and inviting experts to make decisions, we constructed a credit evaluation system to expose users' behavioural characteristics. We found that the three key indexes determining the user’s social credit are personal identification, behavioural characteristics and interaction among friends. Then, AHP was used to determine the weight of each index. Finally, a static algorithm was proposed to compute the credit evaluation system of Weibo users using fuzzy comprehensive evaluation methods.