What is agentic AI in insurance underwriting?

Agentic AI refers to intelligent systems that not only analyze data but also make autonomous decisions based on that analysis. In the context of insurance underwriting, agentic AI plays a crucial role by automating data evaluation, risk assessment, and decision-making processes.

Unlike traditional AI applications that often require human intervention for decision-making, agentic AI systems can operate independently, learning from past outcomes to improve future predictions. This capability allows for faster, more accurate underwriting decisions, ultimately enhancing customer experience and operational efficiency.

Can AI do insurance underwriting?

Yes, AI can significantly automate underwriting tasks. The capabilities of agentic AI tools include:

• Data Analysis: Rapidly processing vast amounts of data to identify risk factors.
• Predictive Modeling: using historical data to forecast potential claims and losses.
• Real-Time Decision Making: Making immediate underwriting decisions based on current data inputs.

Advantages of using AI for underwriting:

• Increased Efficiency: Reduces the time required to assess applications.
• Enhanced Accuracy: Minimizes human error through sophisticated algorithms.
• Cost Reduction: Lowers operational costs by streamlining processes.

Limitations of AI in underwriting:

• Data Dependency: Requires high-quality data for optimal performance. If an insurer’s core system has highly siloed data systems, or isn’t able to serve up real-time or near-real-time data, performance will be lackluster.
• Regulatory Challenges: Must navigate complex legal frameworks governing data use.
• Human Oversight: While AI can automate decisions, human expertise is still essential for complex cases.

What are agentic AI systems?

Agentic AI systems consist of several components that work together to enhance underwriting processes:

1. Data Integration: Aggregates data from multiple sources to provide a comprehensive view of risk.
2. Machine Learning Algorithms: Continuously learn from new data to refine decision-making processes.
3. User Interface: Allows underwriters to interact with the system and review automated decisions.

Examples of agentic AI in insurance underwriting:

• Risk Assessment Models: These models analyze customer data to determine risk levels, enabling tailored policy offerings.
• Claims Prediction Systems: These systems forecast potential claims based on historical data, allowing for proactive risk management.

Is underwriting going away?

The future of underwriting isn’t about elimination, but transformation. While AI advancements may automate many tasks, they’re more likely to enhance the roles of human underwriters rather than replace them.

Human underwriters will continue to play a vital role in:

• Complex Decision-Making: Handling unique or high-risk applications that require nuanced understanding.
• Regulatory Compliance: Ensuring adherence to legal standards that may not be fully automated, and/or making sure that system remain compliant with legal underwriting requirements.

As AI continues to evolve, the underwriting profession will shift towards a model where humans and machines collaborate for optimal outcomes.

Agentic AI for life insurance

In the life insurance sector, agentic AI for life insurance has specific applications that demonstrate its potential. For instance:

• Personalized Policy Recommendations: Utilizing data to tailor policy options based on individual health profiles and lifestyle choices.
• Streamlined Underwriting Processes: Automating the evaluation of medical histories and risk factors to expedite approvals.

• Educational Selling Process: By allowing potential customers to interact with an informed, AI-driven chatbot agent, they can understand the differences in coverage types, and find a policy setup that works for their needs and their budget.

Use Case for AI in life insurance:

How could agentic AI be used for life insurance underwriting?

Let’s say the current rules-based engine at a life insurance company handles simple underwriting cases, but routes anything even remotely complex to a human. This slows down their decisions, increases costs, and frustrates applicants just wanting to get coverage. 

With agentic AI, this life insurer could have a system that could act beyond applying the static rules.

For example, if a borderline case came in with a 44-year-old man with slightly elevated cholesterol and a recent medication change, the AI agent could do things to assist and save significant time for the human underwriter, including:

• Flag potential inconsistencies
• Automatically request certain health records
• Assess risk based on combined information available
• Make a recommendation and log the rationale

This helps the case resolve more quickly, and with feedback, it can get smarter and more effective over time.

Enhancing Underwriting with EIS OneSuite™

EIS OneSuite powered by CoreGentic, integrates agentic AI to redefine the underwriting landscape. It not only automates underwriting tasks, but also enhances compliance and governance through the EIS Knowledge Base™. By embedding AI agents capable of managing workflows autonomously, insurers can achieve significant operational efficiency.

For example, claims processing can be expedited as AI agents autonomously handle low-risk claims, reducing manual intervention and accelerating settlement times. Additionally, the platform’s natural language control allows business users to configure systems using plain-language commands, further streamlining the underwriting process.