Industry: Benefits administration, insurance operations

Focus: Census data reconciliation, variance detection and resolution automation

Technologies: Azure Cloud Services, Azure SQL, MS Fabric Lakehouse, Python/PySpark scripting, Power BI dashboards, workflow automation, HIPAA standardization

Duration: 8-week build for first production-ready deployment, with ongoing enhancements

The Challenge

Complex census file reconciliation, time-consuming manual research

A benefits administration team was managing monthly and quarterly census updates. Each file contained employee counts, premiums, and plan details that needed to be reconciled against carrier invoices and internal systems. The existing process was:

• Manual and spreadsheet-heavy, with data cut/paste steps
• Prone to errors in identifying true variances vs. acceptable differences
• Slow to research and clear variances, delaying billing cycles
• Difficult to scale for new insured groups without adding staff

They needed a repeatable, auditable, and automated way to identify, categorize, and resolve variances while preserving flexibility for human review where needed.

Our Solution

An automated variance detection and resolution platform

We built a modular automation framework that ingests employer census files and carrier invoices, compares records based on configurable business rules, and flags true variances for review.

Key capabilities include:

• Automated data ingestion from standardized or custom employer file formats
• Rule-based matching engine to detect variances by employee, plan, or premium
• Exception categorization (e.g., new hires, terminations, rate changes)
• Resolution workflow with audit logging for approved adjustments
• Dynamic reporting dashboards to track variances over time and identify recurring patterns

This approach balanced automation with human review, ensuring only true exceptions needed manual attention.

Business Impact

Faster reconciliation, fewer errors, and improved client service

• 80% reduction in manual effort per reconciliation cycle (3,200+ hours annually)
• Improved transparency into variances on a monthly and quarterly basis
• Reduced manual research by automatically classifying common variance types
• Lowered error rates in downstream billing by ensuring clean, approved census data before invoicing
  

The team now spends less time manually reconciling data and more time proactively managing client relationships and exceptions.