AI-Powered Solution for Workers' Compensation Claims Review

About Our Client

The Client is a US insurance agency providing general and specialty insurance services to businesses and individuals. Its commercial insurance services include group benefits planning, risk management consulting, and workers’ compensation claims advocacy.

Manual Data Preparation Complicated Claims Reviews

Much of the claims data required for the Client’s workers’ compensation claims services was distributed across the systems of carriers and third-party administrators (TPAs). Different partners used different data formats, naming conventions, and delivery channels, which made it hard to consolidate claims information consistently across the Client’s book of business. Preparing a claim review typically involved extensive communication with partners and manual data preparation, consuming much of the Client specialists’ time before claims analysis even began.

The Client sought to reduce that manual workload through data and workflow automation. To identify suitable technologies and define a practical automation approach, the agency turned to ScienceSoft, a domestic insurance digital transformation partner with specialist competencies in workers’ compensation insurance solutions and artificial intelligence (AI).

Functional Design of an AI-Supported Workers’ Compensation Claims Solution

ScienceSoft’s consultants analyzed the Client’s workers’ compensation claims review workflows and supporting tools. They also interviewed the Client’s claims stakeholders to understand bottlenecks in data collection and review preparation.

Based on the findings, ScienceSoft proposed a solution combining data integration, rule-based automation, and generative AI. The consultants documented the solution concept to guide further functional design and prioritized the capabilities for implementation based on their value to the Client’s operations.

Clear division of automated and manual work

ScienceSoft designed the solution around a tiered responsibility model:

• Rule-based automation would consolidate and normalize structured claims data, detect predefined exceptions, prioritize claims for review, and perform calculations.
• Generative AI would extract relevant data from unstructured sources, summarize claim developments, flag issues for specialist attention, and draft review materials.
• Claims specialists would verify AI-prepared findings and apply valid information in claims analysis and decisions on follow-up actions.

Such a division of responsibilities would let the Client reliably automate preparatory routines while keeping work that genuinely required human judgment with its claims teams.

Unified workers’ compensation claims data

To consolidate claims data from the Client’s diverse carrier and TPA channels, ScienceSoft proposed implementing an automated data integration layer. The solution would use rule-based pipelines to ingest structured claims data and AI-supported pipelines to extract data from documents and communications. It would also normalize incoming data from different formats into the Client’s unified format. This would eliminate much of repetitive data aggregation and reconciliation work and give the Client a consistent view of workers’ compensation claims across partners.

Rule-based automation for predictable tasks

ScienceSoft recommended using rule-based automation for claims review tasks wherever sufficient. This approach would let the Client enhance operational efficiency while avoiding unnecessary complexity and risks related to AI launch.

The consultants prioritized three operations that required substantial manual effort and were predictable enough to automate using straightforward rules:

• Claims triage. The Client could define review priorities as explicit conditions across reserve movements, adjustment activity, claim coding, litigation, or settlement dates. Rule-based engines would apply these criteria consistently across claims and flag cases that require specialist attention.
• Experience rating timing monitoring. Claim losses enter employer experience ratings according to defined schedules. The solution could track relevant dates for each customer and alert claims specialists to upcoming deadlines. This would allow specialists to address reserve optimization with carriers before applicable deadlines and potentially reduce customers’ future insurance premiums.
• Experience modification impact assessment. The Client calculated employer experience modification factors using established formulas and rating parameters. ScienceSoft proposed automating these calculations with rule-based engines and adding what-if scenarios so that claims specialists could assess how individual claims may affect employers’ premiums.

AI assistance for claims analysis and reviews

For claims review tasks involving large volumes of unstructured data and contextual analysis, ScienceSoft proposed adding a generative AI assistant. The assistant could identify relevant facts in claim documents and adjuster notes, compile medical, payment, reserve, and work-status information, summarize claim developments, and prepare findings for specialist review. Claims specialists could also use the assistant to identify potential subrogation opportunities from incident narratives, generate follow-up questions for adjusters, and draft claim review materials for customers. These capabilities would reduce the time specialists spend searching, reading, and compiling claim information, leaving more time for claims analysis and customer communication.

ScienceSoft deliberately kept generative AI in an assistive role to limit operational risks. The proposed solution included retrieval-augmented generation (RAG) mechanisms to provide the AI assistant with controlled access to the Client’s internal data and claims review knowledge. To support the verification of AI-generated findings, ScienceSoft proposed adding source citations. The assistant would reference the documents and pages used to produce its findings, allowing claims specialists to check important information against the original sources before using it in analysis or communications.

Claims review activity and outcome tracking

ScienceSoft proposed implementing an audit trail for the Client’s claims review activities and outcomes, including specialist actions, follow-ups, reserve movements, and experience modification changes. The audit trail mechanisms would automatically capture key events across manual and automated workflows, including AI-assisted operations, and record them in a centralized work log. This would give the Client a traceable record of claims review activities for compliance purposes and evidence of the performed services for customers.

Solution validation roadmap

ScienceSoft identified access to carrier and TPA data as a key feasibility factor for the proposed solution: without reliable data acquisition and extraction, downstream automation and GenAI capabilities could not deliver consistent results. The consultants recommended starting with a proof of concept (PoC) to validate the proposed workflows before committing to full-scale implementation.

ScienceSoft suggested testing an end-to-end claim review support workflow using selected data channels from the Client’s carrier or TPA partners. The PoC would evaluate automated claims data ingestion and normalization, claim review pack assembly, and data extraction accuracy on the Client’s real loss-run formats. This would let the Client confirm the solution’s technical and business viability and establish baseline KPIs for effort and processing time to evaluate future automation.

A Defined Path to Faster and More Efficient Claims Services

The Client received a practical functional design for an AI-supported solution transforming workers’ compensation claims reviews. Thanks to ScienceSoft’s advice on high-value automation opportunities and limits, the Client could capture benefits early without compromising human control over sensitive decisions and actions. The proposed PoC approach would let the Client validate the solution’s technical viability and operational impact before proceeding with implementation.

Once implemented, the solution could accelerate claims data preparation and boost claims specialists’ capacity for high-value analysis, optimization, and negotiation work. Early, technology-supported identification of claims requiring attention could also help the Client capture more opportunities to reduce claim-related costs for its customers.