Guidewire automated tier one. Mid-market still pays $15 per endorsement.

Policy administration encompasses issuing new policies, generating declarations pages, ensuring state-specific form compliance, and collecting initial premiums. It also includes managing mid-term policy changes, endorsements, cancellations, and renewals. These tasks are typically handled by policy services clerks, issuance operations teams, and policy service representatives who ensure policies remain current and compliant throughout their term. This entire process is a cost center, with manual reissuances costing $40-80 and each endorsement costing $8-15 to process.

Modern core systems, including Guidewire, Duck Creek, and Majesco, have largely automated 70-95% of policy administration tasks for top-tier carriers. They streamline policy issuance, form compilation, and basic endorsement processing. In contrast, many mid-market carriers still rely on legacy systems, which necessitate significant manual intervention for these activities. The primary difference lies in the level of inherent automation and the cost associated with managing policy lifecycle events.

Policy administration is a cost center, not a revenue generator. Our model projects manual policy reissuance due to errors can cost $40-80 per incident. A typical personal auto policy undergoes 2-4 endorsements annually, with each manual change costing $8-15 to process. These figures highlight the significant labor costs embedded in 'other underwriting expenses.' Cycle times for manual processes can extend from days to weeks, depending on the complexity of the change and the carrier's operational efficiency.

AI is most mature in automating the remaining exceptions in policy issuance, such as document quality assurance, verifying state-specific variations, and streamlining signature capture. It also powers self-service portals and AI copilots that handle over 80% of routine endorsements, auto-calculating rate impacts and issuing updated declarations. Less mature areas might include highly complex, ambiguous policy changes requiring extensive human judgment, or integrating with deeply entrenched, proprietary legacy systems without modern APIs.

Implementing an AI overlay for policy administration is significantly faster than a full core system replacement, which can take 3-5 years. Our approach focuses on micro-service deployments that target specific activities like policy issuance or endorsements. Expect initial deployments and measurable ROI within months, not years. The process involves integrating AI components with your existing core system, often through APIs, to automate specific workflows without a rip-and-replace project.

The 'operate' approach, focusing on AI overlays and micro-services, allows carriers to target and automate specific labor-intensive activities without the massive, multi-year undertaking of replacing an entire core system. This strategy avoids the high upfront costs, operational disruption, and prolonged timelines associated with monolith replacement. It enables faster deployment, quicker ROI, and the flexibility to modernize parts of your operations incrementally, preserving existing investments while gaining AI capabilities.

Moative integrates with existing core systems like Guidewire, Duck Creek, Majesco, or even legacy platforms, through AI overlays and micro-services. We do not require a full core system replacement. Our solution is designed to plug into your current infrastructure, automating specific activities such as policy issuance exceptions or endorsement processing. This approach allows you to use your current investments while incrementally enhancing capabilities with AI, driving efficiency gains without disrupting your foundational operations.

Our model projects significant ROI from automating policy administration activities. By reducing manual processing costs of $8-15 per endorsement and $40-80 per reissuance, carriers can achieve rapid payback. The automation of these cost-center activities directly translates to reduced operational expenses. Furthermore, improved accuracy and faster processing times can indirectly boost customer satisfaction and retention. Expect payback periods measured in months due to the targeted nature of AI overlay deployments.