Financial institutions face more sophisticated fraud attempts and require new detection methodologies that extend beyond traditional rule-based systems. This article develops a complete architecture for applying artificial intelligence models to Java-based enterprise financial environments. The suggested architecture implements an isolation forest and Long Short-Term Memory (LSTM) algorithms through RESTful APIs running within a services-based microservices ecosystem. Spring Boot services use these models to monitor transactions in real time for digital banking and credit card processing workflows. The architecture also highlights important considerations, such as how to deploy each of these models, how to maximize their performance in large-scale enterprise environments, and how to create a retraining pipeline that will support the continuous retraining of the models to improve detection performance over time. Financial compliance requirements are also considered, which include auditing features and explainability for algorithmic outcomes to support compliance. Performance benchmarks show that the proposed architecture can support typical enterprise transaction volumes at low latency. The proposed architecture can offer financial institutions a maintainable and scalable system for transaction fraud prevention by leveraging automated processes of model updates and version control systems as threat patterns evolve.