Fraud detection is about Finding needles in haystacks and requires reliably labeled instances of fraudulent (needle) and non-fraudulent (straw) behavior. A predictive model can be trained using these labels to learn the underlying patterns in the input variables that best separate Fraud from non-fraud cases, and thereby estimate the fraud-likeness of any future case. Typically, the interesting cases are very scarce, in which case we might have to carefully up-sample the rare class and/or down-sample the abundant class to help the model pay enough attention to the rare class to be useful. But what do we do when labels are not just rare, but are completely absent?
This post first appeared on Elder Research Data Science & Machine Learning Blog, please read the originial post: here