Machine Learning-Based Anomaly Detection for Enhancing Cybersecurity in Financial Institutions

It is important to highlight that the mere financial sectors are experiencing a heightened threat from cyber attackers, and thus, evidential anomaly detection remains pertinent when it comes to protection of confidential information as well as the integrity of financial transactions. The following is a machine learning based anomaly detection architecture which is aimed at improving the security of financial environments. Support Vector Machines (SVM), neuronal network, and Principal Component Analysis (PCA) are some of the cutting-edge algorithms used in the proposed system to extract the features and classify. The studies were designed comprehensively on the basis of a rich set of transactions records. The findings show that the system sinks a high detection rate of $\mathbf{9 5 \%}$ and a low false alarm rate of 5%, which was quite higher to that of the rule-base and statistic model methods. Precision, recall and F1-score, additional measures that provide the examination of the selected algorithms’ efficiency and redundancy, attests to the reliability of the proposed approach. Thus, the system is easily scalable and can handle large amounts of data, which is why it is suitable for detecting anomalies in large and dynamic financial contexts. Further research will be directed at studying various continuous learning approaches that will complement the system’s antecedents to bolster its defenses against emerging threats. Thus, this investigation shows that it is possible to enhance security scenarios in the financial field using machine learning and offers further influence protection plans and tactics.