Fuzzy clustering of clients´ credit risk for futures company

Based on the real clients´ transaction data, several characteristic indices are defined and computed first. These indices then serve as basic variables for clustering. K-means clustering and improved fuzzy clustering approaches are applied to client classification. The final classification is obtained by using intersection-based clustering combination algorithm. The clustering result provides the scientific base for futures companies to improve the clients´ risk management.