Anomaly Detection for Early or Adaptive Fraud Detection

This project investigates unsupervised and semi-supervised machine learning techniques to detect fraud in mobile money transactions. It focuses on identifying anomalous behavior without relying on labeled historical data, with particular emphasis on capturing temporal patterns, adapting to evolving fraud strategies, and enabling early detection of previously unseen fraudulent activities.