# How Genpact helps top US insurers cut contents claims processing times in half with Parallel

## Key highlights

• - Results you can see, with up to:
  • - 55% touchless processing
  • - 50% reduction in cycle time
• - Defendable like-kind-and-quality (LKQ) pricing with citations
• - From human-bounded capacity to unlimited AI capacity and scale
• - In production with top 10 US P&C insurers

## Insurance claims processing standards need an upgrade

Contents claims, the process of replacing lost or damaged property, has been handled essentially the same way for as long as the insurance industry has existed. When a policyholder files a claim, a human reviewer must find the best matching replacement product currently available on the market and determine its price to process a payout.

For top US property and casualty insurers processing millions of claims annually, this means employing or contracting teams of human reviewers to manually research products and price them individually. The process is manual, slow, expensive, and inconsistent.

## How Parallel’s agentic Task API helps Genpact process claims up to 2x faster than the industry average

For each claim, the insurer's system sends three inputs to Parallel: the product name (often incomplete or imprecise), the original retailer if available, and the original price if available. Parallel then orchestrates comprehensive agentic product research to find the best LKQ match, applying the insurer's specific rules:

• - **Price variance constraints** – Matches must fall within a defined percentage range of the original product's price, with special logic when the initial price is missing.
• - **Preferred retailer hierarchies** – Certain retailers are prioritized for specific product categories.
• - **Restricted retailer compliance** – Specific websites are excluded for policy or reliability reasons.
• - **Like-kind-and-quality matching** – When exact matches are unavailable, the system applies similarity criteria across specifications, features, and performance tiers.
• - **Stock availability** – Products must be currently available for purchase.
• - **Rule prioritization logic** – Competing rules are weighed against each other to determine the optimal match.

Parallel returns structured AI outputs for every claim: the matched product, its current price (which becomes the payout if approved), confidence scores, and full citation trails showing which retailers were searched and why a specific match was selected.

When Parallel returns low confidence scores, claims are automatically routed to human review. But instead of starting from scratch, reviewers receive Parallel's reasoning, relevant excerpts, pricing data, and citations, enabling them to make more informed decisions in just minutes.

## Why Parallel

Genpact evaluated multiple web research solutions before selecting Parallel. Most tools couldn't handle the complexity of enterprise-grade matching logic or reliably extract structured data across hundreds of thousands of retailer websites. Other pilot projects didn’t achieve the quality bar required for use in the real world.

Parallel's agentic Task API was purpose-built for this kind of problem: encoding complex business rules into automated web research workflows without requiring custom scrapers for every retailer. Parallel provides Genpact with a single infrastructure layer that can research, match, and price products across the open web programmatically.

## Impact

• - **~50% reduction in cycle time.** The biggest time sink, product research and price matching, is now handled by Parallel, collapsing the end-to-end timeline.
• - **~40% reduction in human review.** Many claims now process automatically, with reviewers focusing exclusively on genuinely ambiguous cases.
• - **Higher matching accuracy than manual review.** Automated workflows search comprehensively across all approved retailers and apply matching rules consistently, helping eliminate the variance that comes with different reviewers taking different approaches.
• - **Complete transparency on every payout.** Citation trails show which retailers were searched, which products were considered, and why specific matches were selected.
• - **Instant policy propagation.** When the insurer refines matching rules, changes take effect immediately across all claims processing.
• - **Better outcomes for both sides.** Insurers gain cost efficiency and consistency. Policyholders get faster claim resolution and LKQ replacements.

Genpact and Parallel together demonstrate that sophisticated pricing research in regulated industries doesn't require human intervention at every step. With the right agentic AI infrastructure, companies can encode expert judgment into systems that operate at machine scale while maintaining, and exceeding, human-level quality.