Generative AI Driven Forecasting for SAP Financial Systems in Hybrid Cloud Banking Ecosystems
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Abstract
As banking enterprises increasingly deploy SAP financial systems across hybrid‑cloud architectures, the demand for accurate, agile, and scalable forecasting of financial metrics, risk exposures and operational indicators has grown dramatically. This paper explores how generative artificial intelligence (AI) models can enable next‑generation forecasting capabilities in SAP financial environments within hybrid cloud banking ecosystems. We propose an architecture combining SAP S/4HANA (or equivalent SAP finance modules) with a hybrid cloud infrastructure (on‑premises + public cloud) and a generative AI forecasting layer that ingests transactional, ledger, controlling and external data to produce forward‑looking predictions—such as liquidity flows, close‑cycle metrics, cost forecasts, risk exposures, and profitability scenarios. A literature review examines generative AI in finance, forecasting with deep‑learning/time‑series, SAP financial systems in cloud and hybrid deployments, and hybrid‑cloud banking architectures. The research methodology proposes a mixed‑method approach: qualitative interviews with banking finance/IT leadership to identify forecasting pain‑points and requirements, and a proof‑of‑concept deployment simulating SAP finance data flows into hybrid cloud and applying generative‑model forecasts with performance metrics compared against classical forecasting methods. We discuss advantages (scenario generation, synthetic‑data augmentation, improved agility) and disadvantages (model governance, data quality, integration complexity) of the approach. Results indicate that generative‑AI forecasting in a hybrid cloud SAP ecosystem achieved enhanced scenario breadth, better early warning lead‑times and acceptable performance overhead under simulation. The discussion covers implementation trade‑offs, governance and operational readiness. The conclusion outlines how banks can adopt generative‑AI forecasting, and future work highlights multi‑cloud extensions, real‑time data integration, and regulatory and ethical governance of generative models. This study offers banking and finance leaders a roadmap for integrating generative AI forecasting into SAP financial systems under hybrid cloud settings.
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