What is agentic AI in insurance?

Agentic AI refers to systems that autonomously make decisions based on data analysis, learning from interactions, and adapting to new information. In the insurance sector, its core functionalities include:

• Predictive analytics: Forecasting risks and customer behaviors.
• Automated decision-making: Streamlining underwriting and claims processes.
• Natural language processing: Enhancing customer interactions through chatbots and virtual assistants.

The primary difference between agentic AI and traditional AI applications lies in the level of autonomy and adaptability. While traditional AI often requires human intervention for decision-making, agentic AI operates independently, allowing for real-time responses to dynamic market conditions.

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What is the application of agentic AI in insurance?

The applications of agentic AI in insurance are vast and varied. Here are some notable agentic AI insurance use cases:

• Claims Processing: Automating claims assessments and approvals reduces processing time and enhances accuracy. Agentic AI can analyze claims data, detect anomalies, and expedite payouts.
• Customer Service: Virtual assistants powered by agentic AI provide 24/7 support, answering queries and guiding customers through policy options, thereby improving customer satisfaction and increasing online sales.
• Underwriting: By evaluating vast datasets, agentic AI enhances underwriting accuracy, leading to better risk assessments and pricing strategies.
• Fraud Detection: Advanced algorithms identify patterns indicative of fraud, allowing insurers to mitigate risks proactively.

These applications not only enhance decision-making, but also significantly improve operational efficiency, enabling insurers to respond swiftly to market demands. EIS exemplifies how agentic AI can be effectively integrated into all aspects of the insurance lifecycle, supporting multiple lines of business within a single SaaS solution.

What are some real-world success stories of insurance companies using agentic AI?

Numerous insurance companies have successfully implemented agentic AI, yielding impressive results. Here are a few case studies:

• Tokio Marine: Using AI and machine learning to reduce fraud and improve claims, Tokio Marine achieved great results, including capturing 5x more fraud. 

• North American P&C insurer: By streamlining FNOL and downstream claims processes with AI, one insurer greatly reduced their loss adjustment expenses. 

These examples demonstrate how agentic AI can drive substantial improvements in business outcomes and customer experiences, positioning insurers as leaders in a competitive market. Furthermore, EIS is the first insurance core system to obtain the ISO 42001 certification for AI management systems, ensuring that the deployment of agentic AI is both responsible and compliant.

What ethical considerations should we keep in mind when developing agentic AI?

As with any advanced technology, the development of agentic AI in insurance raises several ethical considerations:

• Bias in Algorithms: There is a risk that AI systems may perpetuate existing biases in data, leading to unfair treatment of certain customer segments. Continuous monitoring and adjustment of algorithms are crucial to mitigate this risk. Likewise, insurers need to have auditable decisioning in their AI systems, and transparent decisioning guidelines.

• Regulatory Compliance: Insurers must ensure that their use of agentic AI complies with industry regulations and standards, particularly concerning data privacy and consumer protection.

• Data Privacy Concerns: The handling of sensitive customer data requires stringent security measures to prevent breaches and maintain trust.

Addressing these ethical dilemmas is essential for building responsible AI systems that prioritize fairness and transparency.

What is an example of an agentic AI system?

One prominent agentic AI platform for insurance is EIS OneSuite™ powered by CoreGentic™. This cloud-native, API-first solution provides insurers with the tools necessary to enhance operational efficiency and customer engagement. Key functionalities include:

• Integrated Claims Automation: Streamlining the claims process through intelligent automation.
• Customer-Centric Management: Offering personalized policy recommendations based on customer data.
• Real-Time Analytics: Delivering actionable insights that support informed decision-making.

In real-world scenarios, the EIS OneSuite allows insurers to respond quickly to claims, optimize underwriting processes, and enhance customer interactions, ultimately driving business growth.

Agentic AI vs generative AI examples

When comparing agentic AI with generative AI, it’s essential to understand their distinct applications in the insurance space:

• Agentic AI excels in operational tasks such as claims processing and customer service, where decision-making and real-time responses are critical.

• Generative AI, on the other hand, is often used for content creation and simulation, such as generating policy documents or modeling risk scenarios.

Both types of AI have unique strengths in insurance, and understanding their differences can help insurers leverage the right technology for specific needs.