An Automated Alert System for Financial Fraud Detection with Learning Based Models

The rapid digitization has undoubtedly brought im-mense benefits particularly for the younger generation of the 21st century who often find themselves at utilizing digital technologies with ease. However, the elderly who often struggle to adapt to these technological advancements particularly when it comes to tasks such as mobile based transactions. The research in this paper focuses on combinations of various techniques involving the identification of unusual patterns or behaviors in financial transactions such as anomaly detection and continuous learning based models to analyze user behavior and transaction patterns to detect suspicious activities. The paper discusses the capabilities of learning based models, their ability to continuously learn and adapt, and how they can significantly improve the detection rate of fraudulent activities. The potential of the proposed approach lies in offering a real-time, intelligent, and automated solution in order to revolutionize fraud detection by doing outlier analysis. Results analysis shows that the proposed model can effectively identify fraudulent patterns in real time, adapt to evolving tactics, and minimize disruptions caused by false positives.