Abstract

In the era when financial services are going digital and interconnected, the credibility of financial transactions is becoming increasingly important. With cyber threats, insider fraud, and systemic exceptions becoming increasingly complex, financial institutions are under increasing pressure to ensure that every transaction can be verified as legitimate and is compliant and secure, all in real-time. That has driven the buzz around these new, AI-centric network monitoring and tracking technologies, which have evolved at light speed from more complex rule-based tech to machine learning-driven anomaly detection to AI-boosted behavioral analytics. This paper investigates novel approaches that can render financial transaction processes more secure while they are executed through real-time checking features. We identify the relationship between technology advances such as stream processing frameworks, event-driven architecture, anomaly detection with neural networks, federated learning, and blockchain-supported audit trails. In this paper, we start by introducing the main problem in reading and writing big data, which is making real-time tracking difficult, especially in a high-frequency environment, and then focus on some resolution solutions that can provide the monitoring with high speed at millisecond level, high precision from cm -level to dm-level, high degree of integration, unlimited spatial extent of monitoring, scaling up and compliance with law and regulation. Much (NLP)CLEF research presented at the workshop focuses on filling this knowledge gap, including discovering which hybrid supervised and unsupervised machine learning approaches have enabled the development of leading technologies for proactive fraud detection and systemic risk detection. Moreover, the significance of observability tools in microservice-driven financial systems is examined to highlight their importance in lowering latency and enhancing observability. In the methodology section, a simulation-based evaluation methodology using a transaction data set is described, and the results provide benchmark detection accuracy and latency results, comparing our approach with several state-of-the-art monitoring systems. It shows that the hybrid and AI-based monitoring systems can have a detection rate far higher and a response time far shorter than all the traditional ones. Secondly, blockchain, in conjunction with real-time analytics engines, adds an irrefutable and traceable layer of accountability. These findings are also presented in the context of compliance, data governance, and user privacy. Finally, the paper presents a reference architecture that helps financial institutions incorporate several recent approaches seamlessly and develop robust, scalable, and extensible systems for transaction monitoring. The study highlights the importance of embracing real-time monitoring not as a check-the-box requirement but as a mission-critical tool for protecting institutional trust and business resiliency in finance today.