fraud

Many real problems give you confirmed positives but never confirmed negatives, only unlabeled data. The shortcut everyone reaches for quietly biases the model. The signal is naming the regime and its fix. Here is the answer.

The trap is replacing the classifier with an LLM. The real-time, imbalance, and adversarial constraints do not go away. The signal is a hybrid: a fast calibrated model scores inline, LLMs investigate the gray zone off the hot path.