Large Language Model Integration in Enterprise Financial Platforms: Challenges, Patterns, and Emerging Opportunities for Intelligent Customer Engagement

Abstract

Background: Large language models (LLMs) have rapidly transitioned from research curiosities to production components in enterprise financial platforms. Objective: This article examines the architectural and operational challenges of LLM integration in enterprise financial platforms, with particular attention to retrieval-augmented generation (RAG) for domain-specific knowledge grounding, security and data privacy controls, output reliability and hallucination mitigation, and the organizational governance structures required to manage LLM-powered capabilities responsibly. Methods: The study applies a systematic review of architectural patterns drawn from large-scale deployments and synthesizes engineering principles applicable across retail, digital banking, and financial advisory contexts. Results: Four principal engineering challenge domains are identified: knowledge grounding via RAG, security and privacy enforcement, hallucination mitigation through structured validation and escalation, and governance through model lifecycle management and regulatory alignment. Conclusion: Financial institutions that invest in these architectural and governance foundations are positioned to deploy LLM capabilities at scale with the reliability, security, and compliance standards their customers and regulators expect.