AI-Driven Cloud Architecture for Open Banking: Gradient Boosting, LLM Intelligence, and Robotic Data Automation for Advanced Evaluation
DOI:
https://doi.org/10.15662/IJRAI.2025.0806810Keywords:
Open Banking, AI-driven cloud architecture, Gradient Boosting, Large Language Models (LLMs), Robotic Data Automation, Predictive analytics, Financial decision intelligence, Regulatory technology (RegTech), Cloud-native evaluation, API performance analysis, Risk scoring, Automated data pipelinesAbstract
This paper presents an AI-driven cloud architecture for Open Banking that integrates Gradient Boosting, Large Language Model (LLM) intelligence, and Robotic Data Automation to enable advanced evaluation, governance, and decision support across financial services. The proposed architecture leverages cloud-native orchestration to unify structured and unstructured banking data, enabling scalable processing and real-time analytics. Gradient Boosting models perform high-precision risk scoring, fraud detection, customer behavior prediction, and API performance ranking, while LLMs interpret regulatory documents, customer communications, and transactional narratives to generate contextual insights. Robotic Data Automation ensures seamless data ingestion, automated pipeline execution, and continuous monitoring of cross-banking workflows. Combined, these components form a hybrid analytical engine capable of delivering faster, more accurate, and more explainable decision intelligence for Open Banking stakeholders. Experimental validation demonstrates improvements in evaluation accuracy, processing efficiency, and governance compliance compared to traditional cloud or ML-only frameworks. This architecture provides a secure, flexible, and AI-augmented foundation for next-generation Open Banking ecosystems.References
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