PROJECT SUMMARY
A Commercial Property-focused Managed General Agents (MGA) company working with multiple insurers was handling high volumes of broker submissions across diverse property portfolios and geographies.
The organization managed underwriting packages containing ACORD forms, Statements of Values (SOVs), Schedule of Locations, Loss Runs, Broker Cover Letters, and supporting underwriting documents that required extensive validation before submission to carrier underwriting teams.
CHALLENGES
- High dependency on manual validation workflows for underwriting submissions received from brokers and carrier partners
- Fragmented submission formats across ACORD forms, SOVs, Schedule of Locations, Loss Runs, and Broker Cover Letters with no standardized underwriting view
- Time-intensive risk validation processes requiring manual verification across FEMA Flood Zones, Wind Zones, OSHA history, and catastrophe exposure datasets
- Repeated back-and-forth communication between brokers, MGAs, and insurer underwriting teams due to incomplete or inconsistent underwriting packages
- Limited underwriting intelligence and historical risk benchmarking capabilities impacting underwriting consistency, turnaround times, and operational scalability
SOLUTION
- Implemented an AI-driven Underwriting Agent capable of ingesting and processing ACORD forms, Statements of Values (SOVs), Schedule of Locations, Loss Runs, Broker Cover Letters, and supporting underwriting documents, reducing manual document handling effort by 70%
- Enabled GenAI-powered document parsing and automated data extraction to transform fragmented broker submissions into structured underwriting-ready datasets, accelerating submission readiness by 50%
- Built an intelligent normalization layer to standardize underwriting data across multiple submission formats, improving underwriting package consistency and reducing insurer review cycles
- Integrated external intelligence sources including FEMA Flood Zones, Wind Zone classifications, OSHA history, and catastrophe exposure datasets to automate contextual risk validation and reduce manual underwriting research effort
- Developed an AI-powered underwriting validation framework to identify missing information, underwriting gaps, eligibility deviations, and policy inconsistencies before carrier review, improving underwriting quality and governance
- Implemented similar-risk benchmarking capabilities to compare submissions against historical policies, exposure patterns, and prior underwriting outcomes, enabling faster and more data-backed underwriting decisions
- Established a unified MGA-to-carrier collaboration workflow with improved visibility into submission completeness, validation status, underwriting progression, and broker communication cycles, enhancing operational transparency across stakeholders