Using Python's pandas, sklearn, and stats models API, I have trained a logistic regression on a training dataset that tells me whether or not a particular purchase (based on gender, and other features) was fraudulent. I held out a test set, and under a logistic regression all datapoints are predicted to be within %20 percent of unfraudulent (where fraudulent =0, unfradulent =1). In other words, arbitrarily setting a decision boundary to be 50%, all test points are predicted to be unfraudelent. This is because many many more data points are unfraudulent in the training set than fraudulent.
I am new to statistics, and would like to improve my model. What's the next step?