AI-powered Claims Adjudication: Reducing Costs and Enhancing Compliance

Client

US Healthcare Payer and TPA

The client is a large US-based healthcare payer/third-party administrator (TPA). They provide custom-designed health plans and handle their administration, offering comprehensive benefits services that enable self-funded employers to better manage healthcare costs. The healthcare payer sought to reduce costs and improve efficiency and compliance, for which they partnered with Hexaware to implement automation through AI-powered claims adjudication.

Challenge

Manual Bottlenecks and Regulatory Compliance Risks

The client faced significant operational challenges within its claims adjudication and coordination of benefits processes.

• A high degree of manual intervention was required throughout adjudication, increasing the likelihood of inaccuracies and rework. These manual steps slowed processing and made it difficult to consistently apply rules and policies at scale.
• Training and reference materials were dispersed across multiple locations, making it harder for staff to access consistent guidance. This fragmentation contributed to variability in how claims were handled and increased dependency on individual expertise rather than standardized processes.
• Claims adjudication itself was time-consuming, with extended processing cycles that limited the organization’s ability to respond quickly to fluctuations in claim volumes. In parallel, unreliable volume forecasting made workforce planning and capacity management more difficult, further straining operations during periods of higher demand.
• All of these challenges were compounded by tight turnaround times mandated by federal rules and service-level agreements. The pressure to meet these deadlines amplified the impact of errors, inefficiencies, and capacity constraints, creating ongoing risk to regulatory compliance and service performance.

Solution

Implementing AI in Claims Adjudication and Intelligent Document Processing

Hexaware adopted an ‘automation-first’ mindset, leveraging AI-powered technologies to transform claims processing.

• We deployed an AI-driven intelligent document processing solution that leveraged LLM and OCR to extract structured data from various document formats. LLM ensured the quality and reliability of the extracted information, eliminating errors from OCR, reducing manual data entry, and enhancing the accuracy and processing speed of downstream data.
• Intelligent automation using RPA was implemented to automate rule-based adjudication steps, orchestrate system-to-system handoffs, and eliminate repetitive manual tasks.
• Machine learning-based forecasting models predicted volumes more accurately, enabling proactive staffing and surge handling.
• Additionally, we consolidated training guidelines into a centralized manual to standardize decisions and accelerate onboarding.

Human expertise guided our approach by identifying where claims automation could be applied immediately and where targeted process re-engineering was necessary to remove inefficiencies and accelerate delivery. Consolidating training guidelines into a single manual, centralizing business rules, addressing corner cases, and defining escalation paths enabled consistent and standardized decision-making. Human oversight ensured the orchestration of handoffs and approval loops was streamlined, and parallel processing was introduced where feasible to expedite end-to-end processing. This collaboration outperformed traditional methods by significantly reducing manual effort, improving quality, and tightening turnaround times.

Benefits

Efficiency Gains via Automated Claims Adjudication

• 60% reduction in effort (headcount) for routine data-capture and adjudication tasks via LLM’s cognitive decision-making, with measurable quality improvements and lower error rates.
• Faster ramp-up of new hires, reducing time to peak productivity from 18 months to 14 months.
• Intelligent automation bots achieved a 50% reduction in human effort for automated adjudication, enabling 24×7 processing capacity versus standard 8-hour human shifts.
• Improved adherence to turnaround SLAs, with on-time completion within 10 business days increasing from 85% to 90%.
• Reduced exposure to potential penalties through faster, more consistent compliance with contractual and federal turnaround requirements.

Additional benefits included improved auditability of decisions, centralized knowledge reducing rework, and a more resilient operating model scalable for demand spikes. Our AI models were designed with explainability in mind, allowing auditors and compliance teams to trace decision-making processes.

From an ESG perspective, the solution contributed to sustainability by reducing paper-based processes through digital document handling, improving workforce productivity for better work-life balance, and enhancing healthcare cost management. The centralized knowledge management system ensured consistent application of rules, reducing bias in decision-making and improving fairness in claims processing.

Summary

A Roadmap for Scalable Claims Automation

Hexaware partnered with a US healthcare payer to overhaul their claims adjudication and coordination of benefits processes, which were hampered by manual workflows, fragmented knowledge, and compliance pressures. By implementing AI in claims adjudication document processing, intelligent automation, and centralized training resources, Hexaware enabled the client to dramatically reduce manual effort, improve accuracy, and enhance compliance with federal and contractual turnaround times. The solution also improved workforce scalability, auditability, and sustainability, setting a new standard for operational excellence in healthcare administration.

This case is innovative due to Hexaware’s combined approach of automated claims adjudication, consolidated knowledge management, forecasting, and process redesign, delivering rapid cost optimization, higher throughput, and stronger service compliance. The approach is replicable across other clients and industries facing similar manual-intensive processes with strict regulatory compliance and performance requirements.

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