Fraud Detection in Transaction Based on ‎Artificial Intelligence - Hadi, Harem Mahdi; Kareem, Omar Sedqi (International Journal of Scientific World, 2025)
Fraud detection has become a top priority for banks and other financial institutions in a time when digital ‎transactions rule the financial ecosystem. To identify anomalies in real-world transaction datasets, this paper ‎presents a strong hybrid unsupervised learning framework that combines K-Means, DBSCAN, and ‎Isolation Forest. The method circumvents the drawbacks of conventional supervised models, specifically ‎their sensitivity to class imbalance and requirement for labeled data. The suggested approach improves the ‎accuracy of fraud detection by including contextual and behavioral variables like ‎TimeSinceLastTransaction, DeviceUsage, and MerchantPreference. High accuracy 99.20% for K-Means ‎and Isolation Forest, and 99.16% for DBSCAN is demonstrated by experimental results on a dataset with ‎‎2,512 transactions. The models' consensus-based validation strengthens the dependability of the fraud that is ‎identified. This study offers an efficient and scalable anomaly detection method that works well for real-time fraud analytics in settings with a small number of labeled datasets‎.