Architecting Trust: Integrating Large Language Models into Secure Digital Finance Platforms

Abstract

Large Language Models (LLMs) are transforming digital finance by enabling intelligent automation, real-time data analysis, and improved regulatory compliance. However, integrating LLMs into regulated financial environments introduces significant challenges related to trust, explainability, governance, and risk management. This paper proposes a comprehensive architectural framework based on the Digital Finance Reference Architecture (DFRA), an official reference architecture developed by SAP, to facilitate the secure and seamless integration of LLMs within SAP S/4HANA Cloud ecosystems.The proposed architecture supports key financial use cases, including policy interpretation, fraud detection, financial reporting, and continuous auditing. It incorporates a multi-layered integration framework consisting of AI gateways, inference governance, compliance orchestration, and secure data access mechanisms to ensure reliable and compliant AI operations. Additionally, the paper introduces an AI Risk Scoring Model that evaluates the impact of AI-generated decisions and automatically triggers human oversight whenever necessary. By combining governance, transparency, and secure-by-design principles, the proposed approach enables organizations to deploy LLMs responsibly while meeting regulatory and compliance requirements. The framework establishes a trustworthy, scalable, and future-ready roadmap for adopting responsible Artificial Intelligence in digital finance, supporting secure, transparent, and explainable AI-driven financial services.