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AI-Powered FEMA Cost Classification and Reimbursement Optimization

At a Glance

Step-Change in Processing Efficiency

Increased processing speed through automation

Exceeded Classification Accuracy Targets

Achieved >85% classification accuracy with AI

Unlocked and Prioritized Reimbursement Opportunities

Uncovered costs ready for reimbursement and transaction errors to remediate

Challenge

A utility company engaged ScottMadden to address significant risks in its FEMA Category Z (CatZ) cost-recovery process. Employees recorded work activities using unstructured task descriptions in a time-reporting system, making it difficult to determine reimbursement eligibility and ensure compliance with FEMA, state, and local guidelines. Identified non-CatZ transactions lacked a standardized allocation methodology, placing approximately $32 million at risk of non-reimbursement.

Process

  • Developed AI-powered CatZ classification model (CatZerra)
    • Designed a customized solution aligned to FEMA guidelines to improve accuracy and consistency in eligibility determination
  • Established data models and governance frameworks
    • Defined standardized approaches for non-CatZ activities allocation and reclassification across more than 100 projects, improving consistency and auditability
  • Scaled transaction audit and classification capabilities
    • Leveraged machine learning to review more than 220,000 transactions and enable continuous learning, supporting throughput of ~60,000 transactions per day
  • Designed future state, AI-enabled architecture
    • Developed an Azure-based architecture with AI agents to support scalable, real-time validation and enterprise-wide deployment

Result

  • Increased processing capacity from ~10,000 transactions per month (requiring ~10 FTEs) to ~60,000 per day through automation, dramatically reducing manual effort and accelerating FEMA reimbursement timelines
  • Achieved greater than 85% accuracy validated through SME testing and structured quality assurance processes
  • Identified $9.5 million in labor costs ready for submission and flagged more than 100,000 transactions for targeted remediation

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