A Comprehensive Study of Distributed Transaction Handling in Microservices-Based Financial Applications

Managing distributed transactions presents significant challenges as financial systems increasingly transition to a microservices architecture for improved scalability, agility, and resilience. Traditional techniques like Two-Phase Commit (2PC), SAGA, Try-Confirm/Cancel (TCC), and Event Sourcing have strengths but often fail to address the diverse requirements of large-scale, microservices-based financial applications. This paper proposes a hybrid transaction management framework that combines the advantages of these techniques, leveraging dynamic transaction prioritization and a hybrid SAGA-TCC model. The framework categorizes transactions based on criticality, employing SAGA for high-scalability operations and an enhanced TCC-SEGA model for critical transactions to ensure atomicity and real-time consistency. A decision tree approach is used to assign transaction weights based on attributes such as risk, importance, and type, enabling an adaptable and efficient system. This comprehensive solution provides a balance between performance, consistency, and scalability, addressing the complexities of distributed transaction management in modern financial systems. Future directions include exploring blockchain integration for enhanced transparency and immutability, adaptive decision trees for dynamic prioritization, and machine learning models for predictive transaction management to optimize efficiency and resilience further.