Handling Extreme Class Imbalance in Fraud...

Originally published at Riskernel. Fraud is one of the easiest machine learning problems to misunderstand because the target is so rare. In many portfolios, fraud is well below one percent of total events. That means a model can look excellent in offline evaluation while still creating a terrible operational outcome once it meets production traffic. If you are evaluating a fraud vendor or building your own stack, the first thing to understand is that this is not a standard classification problem