Claims Processing Automation Handling 2,000 Daily Claims for a Health Insurance Processor
A health insurance processor was manually adjudicating standard claims — a bottleneck of 2,000 claims per day. We automated the adjudication workflow with UiPath and Power Automate, reducing processing time by 85%.
About the Client
The Challenge
A health insurance processor was manually adjudicating standard claims — a bottleneck of 2,000 claims per day. We automated the adjudication workflow with UiPath and Power Automate, reducing processing time by 85%. The organization faced significant challenges in their existing approach — manual processes, fragmented data, and lack of real-time visibility were costing time, money, and competitive advantage.
The leadership team had attempted to address this before. Previous initiatives either stalled due to technology complexity, exceeded budget, or delivered solutions that users wouldn't adopt. This time, they needed a partner who understood both the technology and the industry context — someone who could deliver quickly and ensure adoption.
The specific pain points were clear: existing systems couldn't scale, reporting was always retrospective (showing last month's data when today's decisions were needed), and the technical team lacked the specialized skills required for modern rpa solutions. Time-to-value was critical — the executive sponsor needed results within one quarter to maintain budget authority.
Solution Architecture
Automation Platform: UiPath for complex multi-system processes + Power Automate for Microsoft-native workflows
Architecture: Attended bots for exception handling + unattended bots for high-volume straight-through processing
Monitoring: Real-time dashboard for bot health, processing volumes, error rates, and exception queues
Our Approach
We designed a phased approach optimized for speed-to-value:
Process Discovery (Weeks 1-2)
Mapped current manual processes with process mining. Identified automation candidates based on volume, error rate, and business impact. Created ROI model for each automation opportunity.
Bot Design & Development (Weeks 2-5)
Designed automation workflows with exception handling paths. Built bots using UiPath for complex multi-system processes and Power Automate for Microsoft-ecosystem tasks.
Testing & Validation (Weeks 4-6)
Tested with production-volume data. Validated exception handling — ensuring bots escalate correctly when encountering unexpected scenarios. Parallel-run with manual process for validation.
Deployment & Monitoring (Weeks 5-8)
Deployed to production with monitoring dashboards. Established SLA metrics: processing time, error rate, exception rate. Created escalation procedures for bot failures.
Optimization & Expansion (Weeks 7-10)
Analyzed bot performance data. Optimized processing paths. Identified adjacent processes for automation expansion. Trained internal team on bot management.
Results
Technologies Used
Key Takeaways
If your organization is facing a similar challenge, here's what we learned:
Speed-to-value matters more than feature completeness. We scoped the initial deployment to deliver measurable impact within 8-12 weeks. The executive sponsor maintained budget authority because results arrived before the next quarterly review.
User adoption determines ROI more than technology selection. We involved end users in design sessions from week 1. The solution reflected their workflow — not a consultant's idea of their workflow. Adoption hit 80% within the first month.
Integration with existing systems is half the battle. The new solution needed to work with the organization's existing technology ecosystem — not replace it. We built integration points that synchronized data in near real-time.
Training by role, not by feature. We trained billing staff differently from managers differently from executives. Each group learned the capabilities relevant to their daily work — not a full feature tour they'd never remember.
Facing a Similar Challenge?
We deliver rpa solutions with measurable outcomes — typically within 8-12 weeks.