Novel approaches to application performance monitoring and optimization in banking systems

Posted: Oct 24, 2022

• Sebastian Gonzalez
• Sophia Nguyen
• Sophia Thompson

Abstract

The digital transformation of banking systems has introduced unprecedented complexity in application performance management, with modern financial institutions operating distributed microservices architectures, real-time payment processing, and multi-channel customer experiences. Traditional application performance monitoring approaches, primarily based on static thresholds and isolated metric collection, have proven inadequate for the dynamic, interconnected nature of contemporary banking ecosystems. The critical nature of banking applications demands not only high availability and performance but also strict adherence to regulatory requirements and security standards, creating a unique set of challenges that conventional APM solutions struggle to address comprehensively. This research addresses these challenges through a fundamentally new approach to banking application performance monitoring and optimization. Our framework integrates three innovative components: quantum-inspired optimization algorithms for real-time system parameter tuning, temporal graph neural networks for modeling service dependencies and performance propagation, and explainable AI techniques for providing interpretable optimization recommendations.