End-to-end insurance transformation powered by AI

How Our AI-Driven Claims Process Works  

Our solution embeds AI directly into the claims management process, transforming raw claim data into structured, explainable assets in real time while guiding adjusters through the reasoning behind each decision.  

1. Intelligent Data Ingestion  

When a new claim is created, all relevant documents such as 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 to ensure completeness and consistency. 

All ingested documents are preprocessed and indexed, enabling AI-driven reasoning and intelligent insights throughout the claims process. 

Some documents, such as the First Notice of Loss (FNOL), can be automatically generated using speech-to-text from claimant's phone calls, as well as from other supporting documents provided by the claimant. 

Additionally, the system can generate a preliminary Damage Assessment document based on analysis of submitted images, such as photos from the accident or incident site, helping accelerate the evaluation process. 

2. Automated Analysis & Case Context  

AI analyzes structured and unstructured data across all claim documents, extracting key information, identifying patterns, and detecting early severity indicators. It also draws on historically similar claims to provide contextual benchmarks, helping adjusters understand how comparable cases were assessed and resolved.  

3. Explainable Reserve Recommendation  

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

• Recommended reserve amount or range  
• Confidence score  
• Key reasoning factors behind the estimate  
• Identified risk drivers (e.g., legal complexity, injury severity)  
• Comparable past claims used as reference points  
• Full traceability to source documents  

This ensures adjusters don’t just receive a recommendation they see why it was made and how it compares to similar cases.  

4. Human-in-the-Loop Decision Support  

Adjusters remain in full control and review AI recommendations directly in their workflow. They can:  

• Accept the recommendation  
• Adjust based on expertise  
• Review supporting evidence and similar cases  
• Escalate complex claims when needed  

The system acts as a guide, not just an output engine supporting faster, more consistent, and better-informed decisions.   

5. Continuous Monitoring & On-Demand Reserving Insight  

The system continuously monitors all claim documents as new information becomes available, updating recommendations, searching for patterns, and identifying missing or inconsistent data. Reserve recommendations are automatically adjusted in real time, replacing rigid periodic review cycles (e.g., 30/60/90-day processes). 

AI can detect data gaps or uncertainties and prompt human intervention only when necessary for example, requesting additional accident images, missing documents, or approvals. Notifications can be sent via email, SMS, or other channels to streamline the process. 

This approach enables on-demand, portfolio-wide reserve adequacy analysis, allowing teams to instantly assess reserve positions across all claims without waiting for traditional deep-dive reviews. By automatically handling routine tasks and data collection, the system ensures claims processing continues efficiently while minimizing employee involvement. 

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

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

This creates a continuous, closed-loop reserving process where every new input improves the system’s understanding and recommendations over time.  

Event-Driven Intelligence  

In addition to documents, the system responds to real-world triggering events that can immediately impact reserve adequacy, such as:  

• Legal demand letters or attorney involvement  
• Medical updates (e.g., surgery or new diagnosis)  
• New evidence or liability information  
• Major claim developments during litigation  

These events automatically trigger reserve reassessment, ensuring the model reacts in real time not just during scheduled review cycles.  

Historical Learning & Comparative Intelligence  

Every new claim strengthens the system’s intelligence by learning from outcomes over time. It continuously references historical and similar claims to improve accuracy and consistency in future recommendations.  

This ensures reserving decisions are grounded not only in current claim data, but also in how similar cases evolved and were ultimately resolved.  

Continuous Monitoring Until Closure  

Claims are continuously monitored and updated until final settlement or policy limit closure. Reserve recommendations dynamically adjust as new information becomes available, ensuring the reserve always reflects the most current risk position.  

Core AI Capabilities  

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

• Intelligent Document Processing (IDP) – extracts and structures data from complex documents  
• Predictive Reserve Modeling – forecasts claim outcomes and recommends reserves  
• Event-Driven Monitoring – detects claim-triggering events in real time  
• Comparative Analytics – benchmarks against historical and portfolio-level claims  

Advanced Intelligence & Transparency  

Transparency and trust are embedded into every recommendation:  

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

These capabilities ensure reserving is not a black box, but a fully explainable and auditable decision process.  

Monitoring and reporting 

AI powered solution can be used for monitoring, reporting and analysis. 

• Emerging trends / risk patterns – identifies increasing risks or new claims patterns, enabling proactive risk management. 
• Portfolio-wide reserve adequacy – shows how well reserves across the entire portfolio align with best practices and historical benchmarks. 
• Loss ratio stability – measures the impact of AI on the consistency of the loss ratio across the portfolio, supporting financial planning and pricing accuracy. 
• Human intervention rate – indicates how often the system requires human involvement, highlighting automation efficiency and time savings. 

Average response time to triggering events – tracks how quickly the system updates reserves in response to new events, ensuring timely adjustments to dynamic situations. 

Business Impact  

The impact is operational, financial, and strategic:  

Operational Efficiency  

• Significant reduction in manual effort required for reserving activities  
• Faster reserve updates, moving from periodic cycles to near real-time adjustments  
• Reduced variability in adjuster decision-making  
• Faster reserve review and approval cycles through AI-assisted analysis  
   

Strategic Benefits  

• Capital Efficiency – Reduced over-reserving improves capital availability  
• Loss Ratio Stability – More consistent reserving improves forecasting and pricing accuracy  
• Reinsurance Optimization – Faster, more accurate insights improve recovery coordination and responsiveness