Deep Neural Network Enhanced Financial Cloud Ecosystem for Predictive SAP Driven Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806801Keywords:
deep neural networks, cloud ecosystem, SAP integration, predictive analytics, financial services, risk modelling, business process automation, enterprise analyticsAbstract
The financial services industry is experiencing increasingly dynamic market conditions, data volumes and regulatory demands, which require faster, more accurate, and more automated analytics capabilities. This paper proposes a combined paradigm of a cloud‑based financial ecosystem that leverages deep neural network (DNN) models for predictive analytics and is tightly integrated with enterprise systems based on SAP S/4HANA (or the SAP ERP/analytics stack). The proposed architecture brings together scalable cloud infrastructure, advanced DNN‑based predictive models (for credit risk, fraud detection, customer behaviour forecasting), and SAP‑driven business‑process integration and analytics workflows. We outline the design of this ecosystem, detail key components (data ingestion, feature engineering, DNN model lifecycle, cloud deployment, SAP integration), and discuss how predictive modelling can feed into SAP workflows (finance, controlling, risk, compliance). In a conceptual implementation scenario, the DNN‑enhanced ecosystem showed significant improvements in prediction accuracy, decision latency, and scalability compared to traditional models and on‑premises analytics architectures. The results highlight how a cloud‑native DNN‑enabled framework complements SAP‑driven analytics to deliver real‑time predictive insights within financial operations. We also examine key challenges including data governance, explainability of DNNs, model monitoring in cloud environments, integration complexity, and regulatory compliance. The paper concludes with recommendations for financial institutions, outlines limitations and sets out future research directions focusing on hybrid‑cloud orchestration, continuous learning, explainable AI and end‑to‑end SAP‑AI operationalisation
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