When insurance personnel speak about underwriting capacity, they usually are referring to underwriting compliance, risk-based capital (RBC) models, or reinsurance. Less commonly, carriers think about operational underwriting capacity. In this context, operational underwriting capacity refers to a carrier's ability to balance speed, risk, and resources to meet the demands of sales and distribution. Key considerations in evaluating operational underwriting capacity include:
- Agent and Customer Expectations – Agents and customers expect policies that can be issued quickly and accurately. For agents, this means strong quoting capabilities and fast cycle times. Policyholders are increasingly seeking instant decisions, wishing to avoid more invasive measures to underwrite policies (e.g., medical exams).
- Talent Considerations – Carrier struggles for underwriting talent are pervasive within the industry, fostering underwriting "hubs" within the U.S. to ensure the ability to attract talent (e.g., Charlotte, N.C.). But access to underwriters is only one part of the equation. Complicated risk also requires specialized skillsets, and all carriers are competing for the best underwriters.
- Risk Assessment Framework – Underwriting has often relied on guidelines and rules-based processing in risk assessment. But much of that framework relies on historical data that leads to inefficient pricing – either overpricing, harming the customer, or underpricing, putting the carrier at increased risk.
The need to address underwriting capacity is not new. Carriers have already pursued rigorous investment in underwriting. In 2024, property & casualty carriers reported a $22.9 billion underwriting gain and industry combined ratio of 96.6%, per AMBest. For life insurers, 2024 saw 3% growth in premium but flat growth in policies. Increased sales in indexed universal life and variable life policies drove premium growth - both products requiring more sophisticated underwriting skillsets.
For carriers, this means pressure to manage expenses, as well as innovative underwriting capabilities, to compete in the market.
One avenue for innovation to create greater operational capacity? Agentic AI. With agentic AI, carriers have the capability to tackle several underwriting challenges.
Leveraging Data to Create More Dynamic Underwriting
Agentic AI can be used to conduct real-time data analysis across a myriad of data sources to better underwrite risk. Behavioral data (e.g., telematics) and IoT sensor insights (e.g., home sensory equipment) have fundamentally changed how carriers can price risk in a dynamic way. For instance, Nationwide has reported that customers enrolled in its usage-based insurance programs tend to pay 20% less than those enrolled in traditional policies. Hippo Insurance has used sensors to detect smoke, carbon, and water leaks, resulting in discounted and customizable products for customers. Agentic AI performs this data analysis to create much more tailored customer segments for pricing purposes.
Although property & casualty is leading the way in this space, expect life and health insurers to follow suit. The opportunity to promote healthier living and improve longevity risk for carriers, using behavioral data and sensors, will improve underwriting. John Hancock is using data from connected devices like FitBit and Apple Watch to provide customers with the opportunity to reduce premium payments while derisking their life insurance business.
Improved Fraud Detection
Agentic AI is capable of identifying fraud before the policy is ever issued. Specifically, it can be trained initially on known fraud practices, freeing existing personnel to focus on more nuanced cases. Over time, carriers can train agentic AI to recognized more sophisticated fraud scenarios. As carriers seek to increase sales, both through premium and policies sold, well-developed agentic AI will be critical to scalability. For example, agentic AI can recognize fraudulent or digitally altered data to either automatically flag or reject an application. This can be particularly valuable in situations where AI can identify fraud more accurately than its human counterpart.
One property & casualty insurer developed an agentic AI PoC focused on identifying policies written by "ghost brokers," individuals who were not authorized to sell policies. In addition, the carrier improved their model's capability to detect misrepresentation, particularly during the "free look" period, to further attack fraud in the underwriting process.
Underwriting Copilot and Training Opportunities
Underwriting triage is a foundation of risk management. This is especially pronounced in complex claims situations, where more experienced underwriters are needed, creating process bottlenecks.
Carriers should consider using agentic AI as both a copilot and triaging tool. As a copilot, agentic AI can accelerate the training process for underwriting trainees, providing real world scenarios and the opportunity to "grade" the underwriter in real time for accuracy. But as a triage tool, an agent can bypass inefficient workflow processes and better manage capacity within the organization. Many underwriter teams are regional – for example, an underwriting team for a captive life insurer may be based in the Southeast to directly support agents within the region. Or there may be a property & casualty commercial insurer that is responsible for a given territory. While that model may continue to be necessary, agents can be used to prioritize and redistribute cases based on need. For example, a commercial property & casualty carrier can use an agent to identify the complexity of a given renewal, assign it to the next capable underwriter, and prioritize them based on the urgency and estimated time to complete them.
Where Do Carriers Go From Here?
While the exact results will be carrier-dependent, a commitment to agentic AI within underwriting will position carriers to be better prepared to meet both financial obligations and consumer sentiment.
As carriers design their underwriting strategy, they should consider if they have the requirements to execute an AI strategy in the space:
- Data Quality and Data Sources – Without the right data, agentic AI is bound to fail. Carriers need to consider what internal and external data sources they want to use, how to remediate their internal data, and how to integrate external data sources into their underwriting platforms.
- AI Governance Structure – At least 30% of AI use cases are abandoned after proof of concept, per Gartner. This is due to companies rushing to do something with AI without any plan. A proper governance structure not only provides a method for evaluating AI use cases at an enterprise level but also provides clear metrics and considerations that will be necessary to address regulatory scrutiny as AI regulation continues to develop.
- Rethinking the Underwriter of Tomorrow – At the core of operational underwriting capacity are underwriters. Carriers need to rethink the entire underwriting function and decide what an underwriter will need to do in the not-too-distant future. For example, will underwriters still need to perform data entry functions or play a more collaborative role with agents or brokers in sales? This exercise typically highlights a key challenge – that there is significant upskilling required within the existing workforce to address underwriting change.
- End-User Experience – As insurance carriers consider the future of underwriting, there must be a recognition that this is not happening in a vacuum. Competitors are also reevaluating their own underwriting processes. As carriers rethink underwriting, they should reconsider the experience with three lenses: agent, customer, and employee. A winning strategy will optimize the experience for all three stakeholders as a means of capturing and retaining all three.
- IT and Process Transformation – Fundamentally, carriers need to reassess their underwriting function and engage in a potential core system modernization. Many carriers have not made investments into their underwriting platforms or modernized processes. For example, a lack of application programming interface (API) connectivity with underwriting platforms may limit the ability to integrate data necessary for agentic AI use cases.
The ability (or inability) of carriers to supplement their underwriting capabilities with agentic AI will affect their profitability and sustainability long-term. Customers, agents, underwriters, and financial investors will demand agentic AI. This cannot be achieved overnight and will require forward-thinking leaders.
