Design and implementation of generative AI-driven data transformation pipelines using spring boot for scalable financial applications

The accelerating development of financial technologies has caused an exponential growth in types of heterogeneous data sources, thus requiring fast, scalable, and intelligent automated transformation mechanisms for these data. Legacy data pipelines usually face challenges related to adaptability to dynamic schema changes, unstructured data formats, and real time processing requirements. Abstract In this paper, we present a design and implementation of a data transformation pipeline powered by Generative AI within the Spring Boot framework in relation to scalable financial applications. The proposed method leverages generative AI models to automate schema mapping, data normalization, anomaly detection, and transformation rule generation to reduce manual effort and make pipelines adaptable.
The architecture uses microservices-based design in Spring Boot (modular, scalability, and fault tolerance). An application for high-throughput financial transactions is designed using batch-processing, streaming messages — message brokers — and distributed processing frameworks for streamlining the hybrid-processing mechanism. What this means is that generative AI models are used for dynamically learning a transformation pattern from historical financial datasets, so we can intelligently deal with semi-structured or unstructured data transactions logs and customer records.
Experimental results show that the transformation accuracy is remarkably improved, processing latency and system scalability are largely boosted by the proposed system in comparison with existing ETL pipelines. Moreover, AI-driven anomaly detection enhances data integrity and compliance critical for financial systems. This study emphasis upon the potential of futuristic generative artificial intelligence while combining it with modern (Spring Boot) backend framework for adaptive, efficient and scalable data pipeline building. The method acts as a solid basis of new age financial applications that require fast detection and smart data processing.