I am working on an anti-fraud project. In the project, we are trying to predict the fraud user in the out time data set. But the fraud user has a very low ratio, only 3%. We expect a model with a precision more than 15%.
I tried Logistic Regression, GBDT+LR, xgboost. All models are not good enough. Step wise Logistic Regression performs best, which has a precision of 9% with recall rate 6%.
Is there any other models that I can use for this problem or any other advise ?