Claims Fraud Detection Agent Workflow

Modernize Claims Fraud Detection: Insurance fraud is among the largest economic crimes, and industry sources estimate roughly one in ten P&C claims involves some element of fraud, adding hundreds of dollars a year to the average policyholder's premium. At the same time, AI-generated documents and manipulated images are making fraud harder to spot, and manual review slows settlement for honest claimants. This professional services offering delivers an intelligent multi-agent solution that scores tampering, links evidence across the portfolio, and routes suspicious claims to investigators—while allowing low-risk, genuine claims to move quickly through settlement.

The result is earlier fraud detection, reduced leakage, and a faster, lower-friction experience for legitimate policyholders.

Intelligent Multi-Agent Framework: The solution combines specialized detection modules and AI agents across three stages. Image forgery detection scores tampering using compression and error-level analysis, copy-move and splicing detection, recapture and device-metadata checks, and reverse-image similarity. Document forgery detection converges structural analysis, semantic validation, and issuer verification into a unified anomaly score. A cross-claim intelligence engine then links documents, entities, and relationships across the portfolio to surface reused evidence and organized fraud rings.

Under a central orchestration layer, each claim receives an explainable tamper or anomaly score with highlighted reasons, and routing rules escalate high-risk claims to investigators while fast-tracking genuine ones. Investigator outcomes feed back into the system to refine detection over time.

Business Capabilities: • Score image tampering using multi-module forgery detection with explainable heatmaps • Detect document forgery through structural, semantic, and issuer-verification checks • Link documents and entities across claims to expose reused evidence and fraud rings • Assign unified, explainable fraud and anomaly scores to each claim • Route claims by risk tier: straight-through, request-evidence, or investigator referral • Provide investigators with explainability packs and supporting evidence • Incorporate investigation outcomes to continuously improve detection • Maintain audit-ready records and decision traceability across fraud operations

AWS-Native Architecture: Built on AWS, the solution uses Amazon Bedrock for foundation-model reasoning and Amazon Bedrock Guardrails for responsible AI policy enforcement, with Amazon SageMaker AI for training and hosting tamper- and forgery-detection models, and Amazon Textract for document extraction. Amazon Bedrock AgentCore supports agent orchestration and secure tool execution, AWS Lambda provides serverless processing, and AWS Step Functions orchestrates multi-agent workflows. Amazon Neptune powers entity-graph analytics for cross-claim linkage, and Amazon OpenSearch Service enables vector similarity search for document fingerprinting. Amazon Simple Storage Service (Amazon S3) stores claims images and evidence, while AWS Identity and Access Management (IAM) and AWS Key Management Service (AWS KMS) enforce least-privilege access and encryption. AWS CloudTrail, Amazon CloudWatch, AWS Config, AWS Security Hub, and AWS Control Tower deliver audit logging, observability, and governance. The architecture is modular, event-driven, and designed for enterprise-grade resilience.