【IBV】2024生成式AI在保险行业中的潜力与应用报告_20页_1mb

Summary of Generative AI in the Insurance Industry

Core Content

The document explores the role of generative AI (gen AI) in the insurance industry, emphasizing its potential to transform customer experiences, product development, and operational efficiency. It outlines the current state of gen AI adoption, the expectations and concerns of both insurers and customers, and the importance of a flexible and responsible AI strategy.

Main Viewpoints

• Gen AI in Customer Conversations: While insurers are achieving early wins in customer-facing systems, the larger value lies in creating enterprise-wide improvements in risk advice, product development, and end-to-end processes.
• Customer Expectations vs. Insurer Perceptions: Customers prioritize tailored products that match their specific risks and preferences, not just improved chatbots or customer support. Insurers, however, often focus on brand and service enhancements.
• AI Adoption Trends: There is a growing pressure to adopt gen AI quickly to stay competitive. Over 77% of insurance executives believe they need to adopt gen AI rapidly.
• Customer Trust and Concerns: Trust in gen AI remains a challenge, with customers concerned about privacy, data security, possible scams, and inaccurate responses. Only 26% trust the reliability and accuracy of gen AI advice.
• Technical Challenges: The insurance industry faces significant technical debt and complexity in its legacy systems, which hinders the integration and effectiveness of gen AI.
• Operating Model Choices: A decentralized operating model is favored by many insurers for faster innovation, with evidence showing better business outcomes such as improved speed to market and customer satisfaction.
• Need for Responsible AI: Insurers must ensure responsible AI practices, including governance, transparency, and security, to maintain customer trust and comply with regulations.

Key Information

Customer Satisfaction and Retention

• Insurers using gen AI significantly in customer systems report a 14% higher retention rate and 48% higher Net Promoter Score compared to those not using it.
• 67% of customers believe insurers should be liable for gen AI errors, and 61% want clear consent and notifications when AI is used.

AI Strategy and Operating Models

• 71% of insurers believe decentralizing gen AI management within business units speeds innovation.
• A hub-and-spoke model is currently the most common, but it is evolving toward more decentralized or hybrid models.
• Decentralized models improve run/build ratios by 2%, speed to market by 14%, customer satisfaction by 9%, and customer retention by 5%.

Technology and Data Challenges

• 77% of insurers are still relying on custom core applications over six years old.
• 52% of executives cite data constraints as a major obstacle to gen AI adoption.
• 85% of enterprises operate across multiple clouds, which complicates data integration and AI model deployment.

AI Governance and Compliance

• Insurers must establish clear AI governance frameworks that cover ethics, compliance, and model performance.
• Continuous monitoring and feedback loops are essential to ensure AI systems remain effective and aligned with regulatory requirements.

Business Impact

• Gen AI can significantly improve product development and customer engagement.
• It can also help modernize IT infrastructure, reduce technical debt, and lower AI production costs.
• The document suggests that open hybrid cloud and open standards are key to enabling AI integration and innovation.

Action Guide

What to Do

• Build more tailored products with flexibility, advice, and data linkage.
• Leverage AI solutions to streamline workflows and improve specific steps in sales and service processes.
• Adopt open standards to enable collaboration and interoperability.
• Spend smarter on AI and cloud investments by using open-source AI and deploying models that can operate across environments.
• Use AI to solve infrastructure problems, modernizing code and automating IT tasks.
• Develop AI governance frameworks that ensure ethical, compliant, and effective AI deployment.
• Enable cross-functional AI precision by empowering business units with access to AI models and data.
• Empower business units to make AI-related decisions, allowing for more agile and targeted innovation.

Conclusion

The insurance industry is at a pivotal moment with the rise of generative AI. While early adoption has shown promise in improving customer satisfaction and operational efficiency, there are still significant challenges in aligning customer expectations with insurer strategies. A decentralized operating model, combined with responsible AI practices, open hybrid cloud infrastructure, and effective governance, is critical for insurers to realize the full potential of gen AI and build long-term trust with customers.