How EY Uses Elasticsearch to Power RAG for Finance Clients

Banks face a growing burden of unstructured data. Regulatory filings, ESG commitments, capital adequacy reports, and multi-year financial statements contain critical information that drives compliance decisions—yet most of it cannot be queried at scale. EY, which provides advisory and technology services to financial institutions globally, saw this as an opportunity to build a production-grade generative AI platform that could give banking clients an analytical edge.

Before EY’s solution, financial teams were spending significant time manually reviewing dense documents to extract key variables. Extracting 14 data points from a 40-page PDF, or comparing figures across reports from different years, required analyst hours that added up quickly. Vendor integrations were slow to ramp up, and homegrown AI components introduced maintenance risk.

EY selected Elasticsearch as the foundation for its RAG architecture, specifically for the ESRE (Elasticsearch Relevance Engine) suite, which combines machine learning models, a vector database, and advanced search and retrieval. ESRE’s embedding model enabled EY to generate and store large-scale vector representations at speed, forming the retrieval backbone for the generative AI pipeline. The platform also integrated seamlessly with LlamaIndex for LLM application orchestration and LangChain for workflow chaining, giving EY flexibility across its AI stack.

The results were measurable from the start. Accuracy across document extraction methods improved by 10–15% compared to previous approaches. Elastic’s RAG setup ran 3x faster than standard Native RAG configurations. EY’s solution handled diverse document formats at scale and could expand across organizational units without re-engineering the core architecture. For one of EY’s flagship use cases—ESG reporting—the platform allowed banks to streamline reporting on internal metrics and supply chain ESG data, meeting regulatory standards with less manual effort.

The deployment set EY up as an active builder of responsible generative AI infrastructure for the financial sector. With Elastic handling the search and retrieval complexity, EY can focus on the domain logic and client delivery rather than maintaining foundational components. The team sees the platform as a durable foundation for expanding generative AI capabilities as financial regulations and client needs continue to evolve.