Fraud Detection Neural Network

This diagram describes a feedforward neural network that assigns a fraud-risk score to a payment transaction. The first block represents the prepared transaction features, which can include amount, merchant category, device signals, and recent account activity. Two dense ReLU layers learn combinations of those signals, while dropout makes the training model less dependent on any one activation. The final sigmoid block produces one score for a review rule or downstream queue. It is a useful overview for risk analysts and engineers because it states the model shape without exposing private feature values or training data. Add thresholds and monitoring blocks separately when describing the production decision process.