Next-Generation FinTech Cloud Framework: Databricks and Azure-Based AI with Gradient Boosting and LLM Integration for SAP-Driven Open Banking and Quality Assurance
DOI:
https://doi.org/10.15662/IJARCST.2025.0806807Keywords:
FinTech cloud framework, Databricks, Azure, gradient boosting, large-language models, open banking, SAP integration, quality assurance, anomaly detection, machine learningAbstract
In the evolving FinTech landscape, open banking and regulatory-driven financial innovation demand cloud-native, AI-powered infrastructures that integrate enterprise systems such as SAP S/4HANA and support large-scale data analytics and machine learning. This paper proposes a next-generation FinTech cloud framework that leverages Databricks on Microsoft Azure combined with a hybrid modelling strategy using gradient boosting and large-language-model (LLM) integration to deliver real-time risk scoring, fraud detection, transaction analytics and quality assurance for an open banking ecosystem. The architecture integrates SAP-driven core banking and back-office workflows, open banking APIs, Databricks data lake and machine-learning pipelines, and Azure services for orchestration and deployment. Using gradient boosting for structured transaction data and LLMs for unstructured text (e.g., chat logs, compliance documentation), the framework enables enhanced anomaly detection, real-time alerts and automated remediation workflows embedded into the SAP ecosystem. A pilot implementation across a mid-sized bank’s open-API platform demonstrates measurable improvements: model accuracy for fraud detection increased by ~17% over baseline, end-to-end time from anomaly detection to remediation reduced by ~35%, and QA defect rate in data exchange pipelines decreased by ~28%. The results indicate that combining cloud-native data/AI platforms with enterprise systems and mixed-modelling approaches can materially enhance FinTech operational resilience and quality assurance. The paper discusses limitations (data governance, model interpretability, integration complexity) and outlines future research directions for federated learning, multi-tenant banking domains and regulatory audit-automation.
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