Broker Login
Appointments

P&C Industry Tips

Your guide to navigating the Property & Casualty sector
Blogs
Get Appointed

Agentic AI in Underwriting: Autonomous Risk Assessment

AI is not new to the insurance sector, but the emergence of agentic AI—systems that can make decisions on their own and learn on their own—is transforming underwriting. Unlike traditional AI models that rely on predefined rules, Agentic AI analyzes real-time data, adapts to emerging risks, and makes binding coverage decisions with minimal human intervention. Projected to grow at a 22% CAGR through 2030 (Compound annual growth rate), this technology is reshaping how MGAs and brokers underwrite complex risks, from cyber threats to climate volatility. Here’s what you need to know.

What Makes Agentic AI Different?

Agentic AI systems, such as those deployed by tech-first MGAs like Encora, go beyond predictive analytics. They operate with three core capabilities:

  1. Autonomy: Independently assess risks, adjust pricing, and bind coverage based on live data streams.
  2. Self-learning: Continuously refine models using outcomes from prior decisions (e.g., claims data, market shifts).
  3. Proactive risk mitigation: Flag vulnerabilities before policies are issued, like identifying fire-prone rooftops via satellite imagery.

For example, Encora’s AI reduced underwriting turnaround time for wildfire-prone properties by 65% in 2024 by autonomously analyzing vegetation density, historical burn zones, and mitigation efforts.

Key Applications in Commercial Lines

Real-Time Risk Segmentation

1. In order to dynamically segment risks, agentic AI consumes non-traditional data, such as drone footage, IoT sensors, and social media sentiment.

  • Case Study: A Midwest MGA used Agentic AI to adjust fleet insurance premiums hourly based on drivers’ real-time behavior (e.g., hard braking, weather conditions).

2. Complex Risk Modeling

The technology excels in markets with sparse historical data, such as:

  • Space tourism: Pricing launch delays caused by solar flares.
  • Cannabis: Adjusting crop coverage for indoor grow rooms using humidity sensors.

3. Adaptive Cyber Underwriting

Agentic AI monitors clients’ network traffic to reprice cyber policies mid-term. If a hospital’s ransomware vulnerability spikes, premiums adjust automatically.

Blogs
Get Appointed

Challenges and Ethical Pitfalls

1. The “Black Box” Dilemma

The decision-making process of agentic AI can be unclear. 43% of brokers distrust AI-generated terms without human-understandable explanations, according to a 2025 Kennedys report.

2. Data Privacy Risks

Autonomous systems accessing sensitive data (e.g., facial recognition in liability claims) may violate GDPR or CCPA if not rigorously audited.

3. Over-Reliance on Automation

In 2024, a European MGA faced litigation after its AI denied flood coverage to a bakery—failing to recognize its elevated floodwalls.

Regulatory Hurdles

  • NAIC’s 2025 AI Guidelines: Require MGAs to document AI decision logic and maintain human oversight for high-stakes policies.
  • EU’s AI Act: Classifies underwriting AI as “high-risk,” mandating third-party audits and transparency reports.
Blogs
Get Appointed

The Future: Hybrid Underwriting Teams

Leading MGAs are adopting a 70/30 model: Agentic AI handles routine risks (e.g., small business GL), while human underwriters focus on complex accounts (e.g., offshore wind farms).

Emerging Trends:

  • Predictive parametric policies: AI triggers payouts for predefined events (e.g., hurricane wind speeds) without claims adjusters.
  • AI-driven E&S markets: Non-admitted carriers use Agentic AI to price “uninsurable” risks like drone delivery liability.

Conclusion

Agentic AI isn’t replacing underwriters—it’s empowering them to act faster, smarter, and at scale. For brokers, the key is balancing automation with human expertise, ensuring clients get both efficiency and empathy in a riskier world.

Blogs
Get Appointed

Share this:

Exit mobile version