Regional Health Insurance Provider – Building an Audit, Balance & Control Framework for Healthcare Data

Introduction

A regional health insurance provider needed a reliable way to validate, reconcile, and govern data flowing through its core operational and claims-processing systems. As the volume and complexity of healthcare data increased, the organization struggled to maintain consistency, completeness, and accuracy across its datasets. Leadership recognized the need for a formal Audit, Balance, and Control (ABC) solution to ensure data integrity across the enterprise.

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The Problem (Gut-Based Decisions)

Before the engagement, the health plan faced several data-quality and operational challenges:

• Data ETL jobs lacked consistent validation steps, resulting in mismatches and reconciliation issues.
• Errors and discrepancies often went undetected until late in the reporting cycle.
• Multiple internal systems produced overlapping datasets, making it hard to verify which figures were correct.
• There was no automated way to “balance” inputs and outputs across the data pipeline, increasing risk of downstream reporting errors.
• QA processes were largely manual and inconsistent.
• Teams had limited visibility into daily or batch-level exceptions.

These issues undermined reporting reliability and created operational inefficiencies across claims, finance, and analytics teams.

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The Solution (How Data Changes the Game)

We designed and prototyped an Audit, Balance, and Control framework—a structured approach to ensuring data integrity throughout the ETL lifecycle.

1. Architecture & Prototype of the ABC Framework

The ABC solution was designed to systematically:

• Reconcile source and target row counts
• Validate numerical balances (e.g., premium totals, claim amounts)
• Identify missing, duplicate, or inconsistent records
• Generate alerts for threshold breaches
• Maintain audit trails for compliance and traceability

This ensured early detection of errors before they reached business users.

2. ETL Development Using SQL & SSIS

We implemented the ABC logic using:

• Transact-SQL programs for validation rules, aggregation logic, and balancing checks
• Microsoft SQL Server Integration Services (SSIS) modules for data movement and transformation
• Configurable workflows to integrate validation steps into each ETL pipeline

This created a durable, automated control process around daily data processing.

3. Test Case Development & Quality Assurance

We developed a structured suite of test cases to:

• Validate reconciliation logic
• Test boundary conditions and exception handling
• Ensure accurate alerting and thresholds
• Confirm that the system behaved consistently across environments

This established a repeatable QA process for ongoing enhancements.

4. Knowledge Transfer & Enablement

To ensure long-term sustainability, we:

• Documented all validation rules and control logic
• Provided walkthroughs of the SSIS modules
• Supported operational teams in adopting the new processes

This reduced dependency on ad-hoc data checks and manual interventions.

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Real-World Example (Specific Client Outcomes)

Following the implementation:

• Data mismatches and reconciliation issues were identified much earlier in the pipeline.
• The reliability of downstream reporting improved significantly.
• The organization gained confidence in the consistency of claims, enrollment, and financial datasets.
• ETL errors that previously took days to identify were surfaced automatically within minutes.
• Teams experienced a reduction in manual data validation and troubleshooting efforts.

The ABC framework became a foundational control layer for the health plan’s data environment.

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Conclusion

By architecting and prototyping a structured Audit, Balance, and Control framework, the organization established a disciplined approach to data integrity across its analytics and operational workflows. The new system delivered automated checks, improved accuracy, and strengthened compliance—laying the foundation for a more reliable and scalable data environment in the healthcare domain.

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