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In the view of credit data falsity and errors problem in real world, and the performance degradation of the credit evaluation model caused by this problem. This paper proposed an outlier detection algorithm, which considered two characteristics of class-imbalance and cost-imbalance. We use an anomaly detection models called EIF to optimize the credit evaluation models. EIF uses the EasyEnsemble algorithm to construct balanced data sets, and train an Isolation Forest model for anomaly detection by the balanced data set with different disturbances. Experiments were performed on UCI German dataset, and the test set with fake data was constructed by correlation. Compared with other anomaly detection algorithms in common credit evaluation models, the EIF-optimized model has a higher F1 score and a lower cost-sensitive error rate. In conclusion, the EIF model is effective in enhancing the performance of the credit evaluation model for forged credit datasets.

Credit risk transmission between cross-platforms is an important issue in the construction of a credit service system. The effect of credit risk transmission between credit entities (nodes) is analyzed in this paper. A heuristic algorithm based on hybrid strategies (HAHS) is proposed to find risk transmission paths and calculate the influence of nodes. Besides, a novel community model is applied to predict the credit risk areas in advance. In detail, the mathematical association structure between credit entities is firstly given in the algorithm, and the breadth first search algorithm is used to find the hierarchical nodes on the credit risk transmission paths. Then, the characteristics of credit risk transmission are analyzed, and the calculation methods of single-path and multipath influence are proposed. Finally, the credit entities are divided into communities based on a greedy strategy considering the characteristics of the credit entity association structure. The threshold control strategy is adopted to find global key nodes among all of the entities and local key nodes in communities, respectively, so as to realize the early warning of credit risk.