A Scalable Hybrid Approach to Detecting Fraud with Machine Learning

Rule-based fraud detection struggles with evolving tactics and fixed thresholds, limiting its effectiveness. This study focuses on detecting SMS fraud using real Call Detail Records and introduces a hybrid machine learning framework combining supervised and unsupervised techniques. XGBoost, trained on expert-validated rule-based labels, benchmarks detection, while an autoencoder, Isolation Forest and DBSCAN identify anomalies and structure fraud patterns. To refine detection, the Fraud Relevance Index integrates model outputs through a weighted sum, prioritizing high-risk cases. The proposed framework improves fraud detection accuracy, reduces reliance on manual labeling and adapts to emerging fraud scenarios.