An Enterprise-Scale Retrieval-Augmented Generative Intelligence Platform for Real-Time Financial Document Synthesis and Regulatory Compliance Automation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P134Keywords:
Retrieval-Augmented Generation, Generative AI, Financial Document Automation, Regulatory Compliance, Microservices Architecture, Semantic Search, Enterprise AI SystemsAbstract
Financial services enterprises process vast volumes of unstructured documents-loan agreements, compliance filings, audit reports, and customer correspondence-requiring synthesis, summarization, and regulatory cross-referencing under strict accuracy and traceability constraints. Conventional document automation relies on rule-based extraction or single-pass large language model (LLM) summarization, both of which struggle with provenance tracking, multi-document reasoning, and auditability demanded by financial regulators. This paper presents the Enterprise Generative Document Intelligence Platform (EGDIP), a layered system architecture integrating retrieval-augmented generation (RAG), microservices-based orchestration, and containerized deployment to deliver scalable, traceable document synthesis for regulated financial environments. EGDIP organizes processing into four cooperating layers: a data ingestion layer supporting heterogeneous document formats and streaming updates, a retrieval and indexing layer built on vector-based semantic search, an intelligence layer combining a fine-tuned LLM with deterministic compliance-rule validation, and a consumption layer exposing synthesized outputs through RESTful APIs and interactive dashboards. The system was deployed on a containerized Kubernetes environment and evaluated on a corpus of 180,000 financial documents from loan servicing and regulatory filing workflows. Results show a 58.3% reduction in document review time, a 39.7% improvement in cross-reference accuracy relative to keyword-based retrieval baselines, and sub-second retrieval latency at the evaluated scale. These findings demonstrate that combining retrieval-augmented generation with deterministic validation yields a practical, auditable path toward generative AI adoption in compliance-sensitive enterprise settings.
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