Anomaly credit data detection based on enhanced Isolation Forest

In view of the real-world problem of falsity and errors credit data, and the performance degradation of the credit evaluation model caused by these problems, we proposed an outlier detection algorithm, which considered two characteristics of class-imbalance and cost-sensitive in credit data. We use an anomaly detection model 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 datasets with different disturbances. On the one hand, the balanced dataset ensures that the class-imbalance problem is solved by undersampling, on the other hand, each sub-model learns from the overall minority class samples in order to solve the cost-sensitive problem. 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.