Insurance GenAI Modernization and Compliance Assessment on AWS

Insurance organizations are increasingly adopting generative AI to improve underwriting decisions, automate claims processing, enhance customer interactions, and detect fraud. However, GenAI initiatives often emerge independently across business units, without consistent governance, regulatory alignment, or scalable architecture.

This lack of control introduces risks related to regulatory compliance, data privacy, model explainability, and auditability—especially in highly regulated environments where decisions must be traceable and auditable. Compass UOL helps insurers assess and modernize their GenAI landscape on AWS by identifying fragmentation, evaluating compliance risks, and defining a scalable and governed architecture. This assessment analyzes current AI usage, data environments, and regulatory requirements to design an AWS-native approach aligned with industry standards.

Leveraging services such as Amazon Bedrock, alongside AWS data and security capabilities, Compass UOL enables insurers to safely deploy GenAI across underwriting, claims, and customer engagement workflows—ensuring governance, traceability, and compliance while improving efficiency and decision speed.

Customers leave with a structured roadmap to scale GenAI securely, reduce compliance risk, and increase operational efficiency while enabling controlled AWS adoption.

Buyer Problem / Business Trigger

GenAI initiatives in underwriting, claims, or customer service without governance or compliance controls Increasing regulatory pressure around AI usage, explainability, and data privacy High operational costs due to manual claims processing and underwriting reviews Fraud detection gaps due to limited use of AI across data sources

Delivery Model

Discovery of GenAI use cases and insurance workflows (underwriting, claims, fraud) Assessment of data environments, AI usage, and regulatory requirements Definition of AWS-native GenAI architecture and governance model Roadmap for compliant GenAI deployment and scaling

Assessment / Engagement Scope

Evaluation of GenAI use across underwriting, claims processing, and customer engagement Assessment of data sources (policy data, claims data, customer data, third-party data) Review of governance, security, auditability, and model explainability Identification of compliance gaps and regulatory risks Design of AWS-native architecture (Bedrock, data platforms, security controls) Mapping of prioritized use cases aligned to business impact

Expected Output / Deliverables

GenAI modernization and compliance assessment report AWS reference architecture for insurance GenAI workloads Governance and compliance framework (auditability, explainability, access control) Risk assessment with prioritized remediation plan Implementation roadmap for scaling GenAI across insurance operations

Customer Decision Questions This offer helps the customer answer:

How can we deploy GenAI safely in underwriting and claims workflows? What controls are required to meet regulatory and audit requirements for AI? Which AWS architecture supports scalable, compliant GenAI in insurance?