In financial services, data is worth more than the products. It should be treated that way.

Regulatory reporting becomes auditable on Snowflake when it is generated from one governed source instead of assembled by hand. Viewnear consolidates fragmented banking, insurance, and asset data into a single governed enterprise warehouse, then automates regulatory and structured external reporting from it, with FINRA, SEC, and SOX-aligned reporting, role-based access, and full audit lineage on every reported number. Self-service analytics on the same source keeps teams deciding on current, reliable numbers instead of stale data.

Snowflake Cortex AI classifies and extracts data from claim documents with the AI functions called directly in SQL, so there is no separate model to host. For a claims processing company that had been sorting documents from many insurance providers by hand, Viewnear built a pipeline where PARSE_DOCUMENT turned each PDF into usable text and layout, AI_CLASSIFY sorted documents into Denials, Verifications, Payments, and Correspondence without rigid templates, and AI_EXTRACT pulled claim numbers, check amounts, dates, and customer details. Classification accuracy rose from 60% to 95%, average per-document classification dropped to 4 seconds, and over 40% of previously discarded documents were recovered.

Cortex AI functions run on the data inside the client's own governed Snowflake account, so nothing is copied to an external service. On the claims work Viewnear delivered on Snowflake Cortex AI, sensitive fields are classified and masked by policy, role-based access limits who can see raw documents and extracted data with separation of duties across processing, review, and reporting, and Horizon Catalog makes every document, classification, and extracted field traceable. The build itself runs in the client's Snowflake account, repositories, and CI, under the client's access controls and change process.