The Reserving Challenge

Traditional reserving processes struggle to keep pace with increasing claim complexity and data volume. Adjusters are required to interpret large amounts of information under time pressure, often leading to variability and missed signals.

Key challenges include:

• Time-intensive reviews – Adjusters spend 40–60% of their time analyzing reserves
• Inconsistent decision-making – Outcomes vary based on experience and workload
• Missed severity indicators – Critical signals in documents are often overlooked
• Document overload – Medical, legal, and claims data are difficult to analyze holistically
• High caseloads – 250+ claims per adjuster drive reactive rather than proactive decisions

At the same time, external pressures continue to increase complexity:

• Social inflation driving higher claim costs
• Economic inflation impacting repair, medical, and legal expenses
• Rising claim severity and operational constraints

From Manual to AI-Driven Reserving

Prime Force replaces static, periodic reserving processes with a continuous, AI-enhanced workflow that combines automation with human expertise.

Instead of relying on fixed review cycles, the system continuously evaluates claims, identifies triggers, and generates data-driven reserve recommendations—while keeping adjusters fully in control.

How the AI-Driven Reserving Workflow Operates

Our solution embeds AI directly into the claims management process, transforming raw claim data into structured, explainable reserve recommendations in real time.

1. Intelligent Data Ingestion

When a new claim is created, all relevant documents—such as First Notice of Loss (FNOL), policy details, medical records, repair estimates, and legal documents—are automatically ingested into the claims management system.

Each claim type follows a predefined set of required documents, ensuring completeness and consistency before analysis begins.

2. Automated Document Analysis

AI processes both structured and unstructured data across all claim documents, extracting key information, identifying patterns, and detecting early indicators of severity or complexity.

3. AI-Generated Reserve Recommendation Report

Once sufficient data is available, the system automatically generates a structured reserve recommendation report, including:

• Recommended reserve amount or range
• Confidence score indicating prediction reliability
• Key reasoning factors influencing the estimate
• Identified risk factors (e.g., legal complexity, injury severity)
• Underlying assumptions used in the calculation
• Full traceability to source documents supporting the recommendation

This ensures every decision is transparent, explainable, and audit-ready.

4. Human Validation and Decision-Making

Adjusters review the AI-generated recommendation within their existing workflow. They can:

• Accept the recommendation
• Adjust the reserve based on expertise
• Escalate complex cases

Supervisors are only involved when authority thresholds are exceeded—ensuring governance without unnecessary delays.

5. Continuous Monitoring and Proactive Updates

The system continuously monitors claims and automatically recalculates reserves when new documents or developments occur—eliminating rigid 30/60/90-day review cycles and enabling real-time responsiveness.

Human-in-the-Loop: AI Augmentation, Not Replacement

The solution is designed to enhance—not replace—adjusters. AI handles repetitive analysis and surfaces insights, while humans retain full control over decisions.

This approach:

• Frees adjusters from routine analysis
• Preserves compliance and oversight
• Builds institutional knowledge through continuous learning

Core AI Capabilities

The solution combines multiple AI technologies to deliver accurate, consistent, and scalable reserving:

• Intelligent Document Processing (IDP)
  Automatically extracts and structures data from complex documents
• Predictive Reserve Modeling
  Uses machine learning to forecast claim outcomes and recommend reserves
• Continuous Claim Monitoring
  Detects triggers such as legal updates, medical changes, or new evidence
• Comparative Analytics
  Benchmarks claims against historical and portfolio data

Advanced Intelligence & Transparency

Transparency and trust are built into every recommendation:

• Explainable AI (XAI)
  Every reserve recommendation includes clear reasoning, assumptions, and supporting evidence
• Portfolio-Level Insights
  Identify trends, risks, and optimization opportunities across the entire claims portfolio
• Proactive Recommendations
  Automatically suggest reserve updates based on emerging developments
• Fraud & Anomaly Detection
  Detect unusual patterns and potential fraud risks early

Business Impact 

The impact is immediate and measurable:

Efficiency Gains

By automating routine tasks and enabling real-time insights, the solution significantly improves operational efficiency:

• 75–85% reduction in time spent on reserving
• Reserve updates reduced from 30–90 days to 1–3 days
• 60–70% reduction in adjuster variability
• AI-supported reserve setting in 2–5 minutes vs. 15–30 minutes manually

Strategic Benefits

• Capital Efficiency
  Reduced over-reserving frees capital for growth
• Loss Ratio Stability
  Lower volatility improves pricing and forecasting
• Reinsurance Optimization
  More accurate and timely notifications improve recovery rates and relationships