Question

A payments company is building a fraud detection model. Fraudulent transactions make up only 0.5% of all transactions in the training dataset. During evaluation, the team notices the model reports 99.5% accuracy but almost never flags real fraud. What is the most likely explanation for this outcome?

Accepted Answer

The dataset is highly imbalanced, so overall accuracy is a misleading metric and the model simply predicts the majority (non-fraud) class.

Answer

The model is overfitting because it was trained for too many epochs on the fraud class.

Answer

The model needs to be switched from supervised learning to unsupervised clustering to detect fraud.

Answer

The inference endpoint is under-provisioned, causing it to drop fraud predictions under load.