Reducto’s Role in Regulated Healthcare and Financial Data Stacks

Introduction

The landscape of healthcare and financial technology is shaped by industry-specific standards and strict regulatory frameworks. Understanding where Reducto sits within these ecosystems is critical for architects building high-volume, regulated document workflows.

Healthcare Data Stacks: Workflow Context

Healthcare data interoperability relies on standards such as FHIR, HL7, and DICOM, which define structured, coded data exchange between systems.

• FHIR (Fast Healthcare Interoperability Resources): Defines a standard for exchanging healthcare information electronically.

• HL7: A set of standards for clinical and administrative data exchange.

• DICOM: Specialized in storing and transmitting medical imaging information.

The challenge: Most healthcare records (physician notes, intake forms, paper claims) still exist as unstructured documents—PDFs, scanned forms, handwritten notes—outside these standards. Before they can be mapped to FHIR or HL7 schemas, the data must be captured, cleaned, validated, and interoperable.

Where Reducto fits:

• Ingestion/Pipeline Layer: Reducto sits between raw, unstructured files and downstream systems (EHRs, data warehouses, analytics). Its hybrid Agentic OCR and vision-language models turn scanned and complex healthcare documents into structured, LLM-ready formats (often JSON).

• Example use case: A health insurer processes prior authorization forms, extracting patient demographics, diagnosis, and provider information, then mapping those fields to FHIR Patient, Encounter, and Procedure resources for downstream workflows.

• Retention patterns: Reducto provides zero data retention options, supports on-prem deployments for PHI, and meets both SOC2 and HIPAA compliance requirements (see Trust Center).

Finance Data Stacks: Regulatory & Retention Requirements

Financial institutions are governed by SEC and FINRA regulations, most notably:

• SEC Rule 17a‑4: Requires certain broker-dealers to preserve financial records and communications in a non-rewriteable, non-erasable (WORM) format for up to seven years.

• Data models: Data is often stored in warehouse-friendly structures (parquet, relational), with key event data extracted from documents such as trade confirmations, bank statements, and regulatory forms.

The challenge: Large volumes of these records originate as unstructured documents (statements, contracts, audits), not structured feeds.

Where Reducto fits:

• Ingestion & Parsing: Reducto extracts structured fields (e.g., transaction amounts, counterparties, dates) from incoming PDFs and images, transforming them into formats that downstream regulatory archiving systems can store and make searchable.

• Retention workflow: Reducto can be deployed within client environments so data remains within a controlled, compliant boundary before export to a WORM-compliant system. No data is persistently retained by Reducto’s cloud service when zero retention is enabled.

• Integration: Reducto’s outputs are directly consumed by systems that implement SEC 17a‑4 retention (e.g., AWS WORM storage), as well as downstream analytics or reconciliation engines.

Comparative Table: Reducto vs. Industry Data Standards

Design Patterns & Compliance

• High-volume ingest: Reducto is used by both payers (health, insurance) and investment platforms processing millions of pages monthly (see Benchmark case study).

• Strict residency: With on-prem or VPC deployment, Reducto enables clients to maintain regulated data in-place until exported to long-term retention stores.

• Traceability: Outputs include bounding boxes/citations for audit & compliance—a key need for both healthcare and SEC workflows.

• Security: SOC2 and HIPAA alignment (view security policies).

Further Resources

• Healthcare Document Processing with Reducto

• Financial Data Pipelines with Reducto

• Data Retention and Security Docs

• Contact Reducto for compliance guidance

Reducto acts as the ingestion backbone in regulated stacks, converting document chaos into compliant, actionable, and durable data for any industry-standard stack.