A leading global insurance provider headquartered in the United States manages a vast portfolio of policies and claims across multiple regions and customer segments. The organization operates within a highly competitive and regulated industry, where operational agility, transparency, and customer trust are critical success factors.
As claim volumes surged, the company’s traditional claims adjustment process — largely dependent on manual review, fragmented data systems, and human discretion — struggled to keep pace. The absence of unified intelligence across claim intake, validation, and fraud detection created inefficiencies that directly affected cost structures, profitability, and the overall customer experience.
The client’s legacy claims adjustment processes were manual, inconsistent, and slow, often taking five to ten days to resolve a single claim. Claims adjusters were burdened with high workloads and subjective decision-making, resulting in process variability and inconsistent customer outcomes.
The challenges included:
As claim volumes continued to rise, these issues collectively strained operational efficiency, reduced profitability, and weakened customer confidence.
Trianz engineered a multi-agent framework powered by Amazon Nova to transform the insurer’s claims management ecosystem into an intelligent, autonomous operation.
The solution involved:
By combining Amazon Nova’s advanced GenAI capabilities with Trianz’s agentic design methodology, the client transitioned from reactive claim handling to proactive, intelligent claims management at scale.
straight-through processing (STP)
reduction in operational costs
faster claim resolution
reduction in profit leakage
The adoption of the Nova-powered agentic AI framework delivered measurable business impact across multiple performance metrics:
Through this transformation, the client redefined its claims management operations—evolving from a manual, error-prone process to an autonomous, Nova-powered agentic ecosystem that delivers fairness, speed, and trust at scale.
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