Abstract
Anomaly detection in real-world domains often involves highly imbalanced datasets, where rare but critical events are challenging to identify without distorting the natural data distribution. Many existing approaches rely on resampling techniques or prioritize accuracy-based metrics, which may not accurately reflect the model’s true effectiveness in such scenarios. This paper examines the application of the FraudX AI framework, initially designed for financial fraud detection, to both network intrusion and credit card fraud detection under natural class imbalance. The framework integrates Random Forest and XGBoost classifiers using a weighted ensemble, combined with calibrated threshold tuning and SHAP-based explainability. Experiments conducted on the CIC-IDS2017 and European credit-card datasets demonstrate that the model achieves high recall and AUC-PR without applying data balancing techniques. These results highlight the framework’s robustness and generalizability for detecting anomalies in complex, imbalanced environments.