Customer credit modeling and credit granting method based on hybrid algorithm of collaborative filtering and social network - Ke ZHANG; YueJia SUN; Hai HAN (Dianxin kexue)
With the expansion of market demand for customer credit screening,the traditional FICO credit modeling method relies on a large number of historical credit behavior of trustees,and the limitation of weak ability to identify group attributes is constantly emerging,which causes the problem of cold start of credit scoring.Based on the identity characteristics of the trustee,the initial score through collaborative filtering method was firstly granted,and then the credit evaluation was modified through social network trust graph and group clustering learning results to solve the credit cold start problem during the transition period of scoring.The experimental results show that the credit pre-evaluation error of this method is low and can be smoothly transited to formal evaluation.This method can cultivate good credit habits of trustees in the initial stage of credit,guide the virtuous circle of credit behavior.