Modernizing Monoliths: Transitioning Legacy Financial Systems to MicroServices

Abstract

Financial institutions, being the relevant banking industry players, must respond to the development in the banking sector toward an increasingly data-driven and fast-paced environment with the transition from legacy monolithic systems to the microservices model. In today's financial environment, where times are real and where data is processed without delay and in a cost-effective fashion, legacy systems are not flexible enough and monolith-driven; they are inadequate. One solution it provides is to divide a complex system into a series of independent services that can be scaled and updated without affecting the other parts of the system, thus providing flexibility and efficiency. This study analyses financial institutions' difficulties with upgrading their legacy systems using technologies such as Apache Kafka and Apache Spark and where to put the data in real time. It also involves integrating AI-driven microservices, notably generative AI, to automate customer service, improve risk management, and improve predictive analytics. Microservices are compared to the inefficiencies of a monolithic system, and the benefits of the microservices are discussed, namely, modularity, scalability, fault tolerance, and security. The other aspect of the paper also explores the best practices for implementing microservices, like choosing the right technologies, robust security measures, and proper management of the resources while migrating to them. This study aims to put forward a roadmap for the potential modernization of systems in financial institutions to stay competitive and react to the market dynamic through detailed analysis of microservices architecture and case studies.