Abstract: In the current digital era, financial cybersecurity is crucial, where the financial industry is vital to the world economy. The frequency and sophistication of cyber-attacks are mounting, making difficult for traditional fraud detection systems to stay up with the changing threats. As a consequence, using Artificial Intelligence (AI) based Machine Learning (ML) approach in fraud detection system, the presented article offers an enhancing financial cybersecurity. The collected input data from financial and transactional data gets pre-processed with the help of data cleaning, data normalization, which aims to remove noise and improve the quality of input data. For effectual forecasting of fraud prediction, the proposed model uses a novel hybrid rule based and isolation forest approach. This rule based scheme ensures regulatory compliance and interpretable alerts, while the isolation forest proficiently isolates anomalies without requiring labeled data. Overall, the analytical evaluation on real world financial transactions system is ensured by the introduced topology, which accomplishes lower errors and higher accuracy of (97.45%) with a significant reduction in false positives and faster decision making compared to the traditional supervised learning models.
Keywords: Financial cybersecurity, cyber-attacks, fraud detection systems, Artificial Intelligence, Machine Learning, Hybrid rule based and isolation forest.
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DOI:
10.17148/IJARCCE.2025.14718
