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Abstract
Fraudulent financial transactions pose a significant threat to global financial systems, demanding timely and intelligent countermeasures. This study presents a short research exploration into the development of AI-driven systems for early fraud detection through transactional anomaly detection techniques. Focusing on machine learning and statistical profiling, the study odellin transactional behavior to detect deviations indicative of fraud. Leveraging prior research, the paper identifies key models and methodologies with proven success and proposes a real-time anomaly detection architecture for enhanced financial cybersecurity. Results show that unsupervised models such as Isolation Forest and autoencoders are well-suited for detecting subtle fraudulent behaviors, even in imbalanced datasets.
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