Accelerating Legal Claim Processing with AI
Extraction and Separation
Solution
The project had an implementation deadline of 1 week to achieve the following:
- Separate individual receipts from large scanned batches of over 500 thousand reports
- Extract receipt numbers from 15 document types
A leading legal services organization faced growing pressure to process accident reports quickly and accurately. Traditionally, every report required review by a legal professional to determine a fair compensation amount. This process was:
- Slow – claims often took days or even weeks to complete.
- Costly –the reliance on highly trained professionals drove up operational expenses.
- Unsustainable at Scale – with over 500,000 reports waiting for review, the backlog created frustration for both clients and staff.
The organization needed a solution that could keep pace with rising caseloads, reduce costs, and deliver consistent, defensible outcomes.
To address these challenges, Aluma deployed an AI-powered solution leveraging large language models (LLMs). The system was designed to read and analyze accident reports, evaluate the details, and output a recommended compensation amount—all without requiring manual review.
Key aspects of the solution:
- Rapid Deployment – Implemented and operational in just one week.
- Advanced AI Capability – Pushed the boundaries of what LLMs can achieve in complex legal reasoning.
- Automation at Scale – Enabled processing of 500,000 cases consistently and efficiently.
By automating the initial assessment stage, the organization was able to drastically reduce the load on legal professionals while still maintaining accuracy and fairness in recommendations.
Impact
Dramatic Speed Improvements
Claims that previously took days of manual review could now be processed in minutes, significantly reducing client wait times.
Cost Efficiency
By reducing reliance on legal professionals for initial claim assessment, the firm lowered operational costs while freeing staff for higher-value tasks.
Scalability at Volume
The AI system processed 500,000 accident reports efficiently, demonstrating its ability to handle large-scale workloads without bottlenecks.
