Examiners spend 60% of cycle time on reserve memos no one reads.

Claims adjudication involves reviewing investigation findings and documentation to determine coverage, liability, and initial reserve amounts. Examiners then approve or deny the claim, negotiating settlements and issuing payments. Reserving for indemnity and loss adjustment expenses is a critical sub-process. This activity is primarily executed by senior adjusters, claims managers, and reserves analysts, who spend significant time analyzing complex cases and ensuring regulatory compliance.

Traditional claims management platforms provide the system of record. Moative operates as an AI operating partner, embedding specialized agents directly into adjudication and reserving workflows. Instead of selling software licenses, Moative delivers outcomes like reduced cycle time and improved loss ratios. Our agents act as co-pilots for examiners, automating routine tasks and flagging anomalies within your existing Guidewire or similar platform, without requiring a rip-and-replace approach.

Our model projects AI can significantly cut manual settlement review time by approximately 50% through automated document generation and pre-population. For the overall adjudication process, AI co-pilots reduce time examiners spend on factual review and memo generation. By streamlining documentation and decision support, the end-to-end claims cycle time for personal lines can see substantial reductions, leading to faster claim resolution and improved customer satisfaction.

AI excels in predicting claim costs based on injury type, geography, and historical data, optimizing reserve adequacy. It is highly effective in automating recurring tasks like settlement document generation and identifying potential fraud or negotiation errors by flagging unusual settlement amounts. Current limitations involve complex, highly subjective cases requiring nuanced human judgment, and situations with sparse historical data for AI training. AI assists, but human oversight remains essential for ethical decisions.

Moative's AI operating partner model focuses on rapid integration with existing core systems. Initial deployments for specific high-volume workflows, such as automated reserve analysis or settlement documentation, can often be operational within 8-12 weeks. Full integration across the entire adjudication and reserving spectrum is phased, allowing measurable impact and iterative refinement while minimizing disruption to ongoing operations. Our approach prioritizes delivering early value.

Choosing an operating partner means paying for results, not just software. Moative's model aligns our incentives directly with yours: reduced cycle times, improved loss ratio, and higher subrogation recovery. We embed dedicated AI agents and teams to continuously optimize these outcomes, becoming an extension of your operations. This eliminates the burden of managing new software, internal development, and the risk of unproven technology initiatives, offering a clear path to measurable value.

Moative's AI agents are designed to integrate seamlessly with your existing claims management platforms, such as Guidewire, OneShield, or Sapiens. We utilize secure APIs and data connectors to ensure our co-pilots enhance, rather than replace, your current infrastructure. This allows our AI to access necessary claim data, process information, and present recommendations or automate tasks directly within the examiner's workflow, maintaining data integrity and system security.

Our model projects significant ROI from optimized claims reserve setting. AI-powered models improve the accuracy of reserves, minimizing both over-reserving (tying up capital) and under-reserving (leading to financial surprises). This accuracy directly impacts statutory surplus and loss ratios. By reducing manual processing time (projected up to 50% for settlement review) and improving decision quality, carriers typically see payback within 6-12 months through expense reduction and capital efficiency gains.