ANOMALY DETECTION IN FINANCIAL TRANSACTIONS USING ADVANCED MACHINE LEARNING TECHNIQUES (CAHIERS MAGELLANES-NS, 2024)
Anomaly detection in financial transactions is vital for identifying fraud and maintaining system integrity. Advanced machine learning techniques, such as deep learning, ensemble methods, and unsupervised learning, have significantly improved anomaly detection capabilities. Deep learning models, including autoencoders and recurrent neural networks, adeptly capture complex patterns and dependencies, enhancing the detection of subtle anomalies. Ensemble methods combine multiple models to increase accuracy, while unsupervised learning approaches, like clustering and dimensionality reduction, identify novel anomalies without labeled data. These techniques collectively advance the effectiveness of financial anomaly detection systems, reducing false positives and better combating financial fraud.