Real-Time AI-Enabled Anomaly Detection System for Preventing Financial Fraud
Abstract
The problem of financial fraud remains a major threat to economic systems of any country, affecting billions of dollars every year in losses, with traditional guarding tools based on rules falling short of the complex attack vectors pursued by hackers. The major research goal is to come up with and test a real-time AI-powered anomaly detection system that will accurately detect and block potential financial fraud without incurring a high rate of false positive detections. Our approach comprises the integration of ensemble machine learning, such as Isolation Forest, LSTM neural networks, as well as gradient boosting algorithms along with real time streaming, Apache Kafka and distributed computing systems. A large transaction dataset with more than 2.5 million entries, including simulated fraud trends and actual transactions was used to test the system. Experimental results shown assert that the proposed system obtained 97.3% accuracy, 94.2% recall, and 93.8% precision in fraud detection with an average of 1.2 seconds in detecting fraud per transaction. The system shows much better results than investing in the known rule based systems or separate machine learning models, resulting in the decease of false positive rates by 40% in comparison with the currently available interventions. This study would not only help move financial security systems one step further but allow it to create the framework of real-time fraud prevention that could be applied in different financial institutions and with different types of transactions.