Modernizing the Insurance Industry with Generative AI and Data Analytics

Common Issues Faced by Insurance Companies

• Difficulty in moving AI initiatives from pilot to production
• Lack of internal skills for AI and generative AI
• Data readiness issues due to legacy systems and merger/acquisition challenges
• High upfront costs for engineering solutions
• Prioritizing business-as-usual over innovative projects

DXC and AWS Partnership

• DXC's position as a leading vendor for insurance core systems and data understanding
• Collaboration with AWS to build a pre-built solution on top of AWS Insurance Lake
• Providing a platform and use cases with minimal customization
• Enabling pilot cases to be started in 2-3 months

Key Components of the Platform

1. Source system connectors
2. Data lake and insurance data model
3. Data catalog and explainable data
4. Pre-built BI reporting
5. APIs and event-driven integration
6. Machine learning and generative AI models

Distribution Use Cases

• Cross-sell and up-sell
• Churn prevention
• Agent training
• Win-back
• New business

Data Sources for the Use Cases

• Customer interactions (portal, call center)
• Third-party data (endorsers, open data)
• Customer behavior data (past claims, products owned, family connections)

Example: Agent Interaction Support

1. Selecting the right customer to contact
2. Defining the product combination to propose
3. Generating content and scheduling the appointment

Agent-Centric Recommendations

• Commission forecast impact
• Prioritized customer and product recommendations
• Feedback loop to improve future recommendations
• Customizable communication content and scripts
• Integration with existing agency systems

Conclusion

• DXC and AWS provide a pre-built solution to accelerate insurance industry modernization
• Leveraging generative AI and data analytics to support various distribution use cases
• Enabling a win-win situation for agents and insurance companies