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Empowers HR teams to deliver fast, accurate, and personalized employee support through conversational AI, automated knowledge management, and seamless HR system integration.
Vehicle insurance claim agent
Multi-agents solution that accelerates claim resolution by digitising and managing the end-to-end process, from intake and validation to negotiation and settlement, reducing delays, improving accuracy, and enhancing customer satisfaction
#Insurance #Cost Saving #Increase Efficency
Business Challenge
Processing vehicle damage claims is often slow, fragmented, and heavily manual. From initial intake to payout, insurers rely on disconnected systems, paper-based workflows, and human validation—leading to delays, errors, and rising operational costs. Verifying coverage, assessing damage, and detecting fraud require intensive effort, while customer expectations for speed and transparency continue to grow. As volumes increase, Property & Casualty insurers face mounting pressure to modernize claims handling without compromising accuracy, compliance, or customer trust.
Solution Overview
Our Vehicle Insurance Claim Agent is a prebuilt AI solution that streamlines and automates the full damage claims lifecycle for Property & Casualty insurers. From speech-based incident intake to policy validation, evidence analysis, and settlement initiation, the application reduces manual effort and accelerates resolution. Powered by a modular architecture of specialized agents—including vision, fraud, pricing, and summary modules— Vehicle Insurance Claim Agent integrates seamlessly with internal insurance systems and external sources, such as organizations responsible for maintaining driver databases. The result is faster, more accurate claims processing, improved fraud detection, and a significantly better experience for both insurers and policyholders.
The solution's main features include:
Automated Identity and Policy Verification
Validates claimant identity and policy details by connecting with internal insurance systems and external authorities (e.g., police databases).Evidence Collection and Assessment
Collects, analyzes, and verifies incident descriptions, images, videos, and supporting documents to ensure completeness and consistency.Damage Evaluation and Cost Estimation
Automatically classifies damage severity and calculates estimated repair costs using real-time market data—visible only to the insurer until approval.Fraud Detection and Prevention
Identifies manipulated, reused, or AI-generated media and documentation to mitigate risk and ensure claim authenticity.Claim Summary and Workflow Automation
Consolidates validated data and auto-populates structured claim forms to accelerate processing and reduce manual effort.
Technical Implementation
This Generative AI solution was built with:
AI-Powered Vision & Multimodal Models
Fine-tuned vision-language models analyze submitted images and videos, detecting damage and classifying its severity. Optical Character Recognition (OCR) extracts key data from documents, receipts, and license plates. Additionally, speech-to-text models enable voice-based claim intake, using language tailored to the insurance domain.Seamless Integration
APIs enable integration with insurer systems for real-time pricing and with external sources for identity and policy validation (e.g. police, DVLA).Intelligent Workflow Automation
A state-managed orchestration layer coordinates interactions between agents, maintains context throughout the process, and enables human-in-the-loop interventions when necessary. It also supports asynchronous operations for tasks such as external data lookups or manual approvals.Optimised LLM Use
Generative models are applied to real claims data to support core processes. Prompt engineering techniques—such as few-shot learning, in-context examples, and chain-of-thought prompting—enhance tasks like summarisation, document generation, and policy reasoning.Fraud Detection
AI algorithms detect reused, manipulated, or synthetically generated content. Multimodal consistency checks compare narrative, media, and documentation to identify potential discrepancies and flag suspicious claims.
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