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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.