Detection

A deep learning project focused on anomaly detection in financial transactions. The system uses a one-class autoencoder trained on normal data to identify unusual or fraudulent activities with high precision.

Key Features:

Detects anomalies in large-scale datasets.

Handles highly imbalanced financial data.

Achieved 99.62% accuracy with strong recall and precision.

Provides a scalable framework for fraud detection.