I explored the potential of agentic AI across four foundational insurance functions in a previous blog article. Building on that discussion, this article takes a focused look at one of those pivotal functions – insurance underwriting.
With the adoption of AI agents, insurance underwriting is poised to evolve.
Let’s delve into how these advanced systems operate, outline the tasks that can be delegated to AI agents, discuss the benefits insurers can realize and highlight challenges you would not want to overlook.
What is an agentic AI underwriting agent?
Traditional underwriting has always been a complex and time-consuming process, requiring manual data collection, extensive risk analysis and careful policy customization. With the rise of agentic AI, these constraints are being redefined.
An “AI underwriting agent” in the context of agentic AI is an autonomous AI system capable of orchestrating the entire underwriting workflow.
Unlike large language model (LLM)-powered chatbots, which mainly respond to queries, agentic AI agents persist over time, learn from new data and refine their decision-making processes without constant human intervention. They are goal-oriented, executing predefined objectives with multistep reasoning and adaptive strategies.
But agentic AI agents do more than automate simple tasks. They integrate LLMs, traditional machine learning and structured decision frameworks to create governed, explainable and trusted decisions. The result is an underwriting process that is not only faster, but also more intelligent and transparent.
Related: Agentic AI rewrites insurance underwriting: See how it works
How agentic AI underwriting works: The process
Agentic AI systems in underwriting operate as intelligent, self-directed agents that manage data-intensive and decision-heavy tasks. Here’s how the process typically unfolds:
- Data orchestration. First, an AI agent collects and unifies data from diverse sources, including medical and financial records, customer profiles, historical claims data, policy applications, field surveys and third-party data.
- Risk analysis. Using advanced algorithms, the AI agent evaluates risk factors in depth, learning from prior analysis, and uncovers patterns or anomalies that may affect policy terms or pricing.
- Policy suggestion. Based on its assessment, the AI agent suggests optimal policy terms tailored to the applicant’s risk profile – while balancing coverage and cost.
- Continuous refinement. As new data becomes available, the AI agent refines its decisions, learning from both successful and exceptional cases.
- Integration with human expertise. While an AI agent can handle standard cases autonomously, human underwriters are needed to focus on final approvals as well as complex, exceptional scenarios.
Tasks managed by AI agents in underwriting
Agentic AI can autonomously perform a variety of underwriting tasks, such as:
- Automating the collection and verification of applicant data from multiple sources – both structured and unstructured.
- Assessing risk factors based on a comprehensive analysis of things such as medical and financial information, or publicly available information and details obtained from satellite imagery (in the case of home or business insurance).
- Recommending policy terms, pricing structures and discounts.
- Screening for inconsistencies or potential fraud through pattern recognition.
- Providing real-time guidance or decision support to human underwriters on complex cases.
By managing these tasks, agentic AI systems enable an efficient underwriting process.
Why adopt AI agents?
Insurers that adopt agentic AI for underwriting could gain a host of benefits.
For starters, they may see a significant reduction in processing times. That’s because AI underwriting agents can process applications rapidly, freeing human underwriters to focus on exceptions and approvals. Similarly, the automation of routine and repetitive tasks typically leads to lower costs and better allocation of human resources.
Incorporating AI-driven assessments can result in more precise risk profiles and policy recommendations, which then minimizes risk exposure. Providing faster response times and more tailored policy offers also improves customer satisfaction and retention.
Similar to human underwriters, AI underwriting agents learn continuously and refine their decision-making over time. This leads to ongoing improvements in accuracy and effectiveness.
Challenges and considerations
While the benefits of AI underwriting agents are substantial, insurers also need to navigate certain challenges when integrating agentic AI into underwriting. For example:
- Governance and oversight. AI systems must operate within strict AI ethics standards and regulatory frameworks, requiring robust AI governance mechanisms to ensure transparency and accountability.
- Decisions made by AI agents need to be explainable to regulators, customers and internal stakeholders to build trust and enable compliance.
- Data management, including data quality and integration. The accuracy of AI-driven underwriting depends on the quality and completeness of data, making this a non-negotiable capability for which insurers must invest.
- Human-AI collaboration. The balance between automation and human expertise is crucial. Insurers must clearly define the roles of AI agents and human underwriters, especially for complex or exceptional cases.
- Change management. Transitioning to agentic AI requires organizational change, staff training and a strong digital strategy to ensure smooth adoption.
An agentic AI future
Agentic AI is set to revolutionize underwriting by automating data-driven processes, refining risk analysis and delivering operational efficiencies. Its adoption promises faster, more accurate and more customer-centric insurance services.
However, successful implementation demands strong governance, a focus on data quality and thoughtful integration between AI agents and human professionals. By meeting these challenges head-on, insurers can unlock the full potential of AI-driven underwriting and secure a competitive edge in the market.
