Our AI-Powered FNOL Automation Solution streamlines the entire First Notice of Loss process by intelligently analysing multiple data sources to extract and validate key claim details.

When a new FNOL report is submitted, the solution prompts the user to upload photographic evidence and any witness reports. Our advanced computer vision and natural language processing capabilities then analyse these data sources to retrieve crucial information, such as the description of the incident, the damaged objects, the date, and the cause of loss.

Next, the user is asked to upload the relevant insurance policy. The solution will automatically identify the policy number and validate the input data against the policy details to determine if the reported damage is covered.

Based on this comprehensive analysis, the system provides a confidence score to indicate the likelihood that the claim is valid and eligible for coverage. This empowers your claims team to make informed decisions and initiate the appropriate workflows, accelerating the time to settle valid claims and enhancing the overall customer experience.

The modular and scalable architecture of our solution ensures seamless integration with your existing claims management systems, while also providing the flexibility to adapt to evolving FNOL requirements. With this transformative technology, you can unlock new levels of efficiency, improve claims processing, and deliver exceptional service to your policyholders.

Transparency and Explainability: The AI-generated nature of the insights must be clearly communicated to users, with prominent labeling and disclaimers to ensure they understand they are interacting with an AI system.

Confidence Scoring: The solution provides a confidence score to indicate the likelihood that the claim is valid and eligible for coverage. This empowers users to make informed decisions, while also highlighting the limitations of the AI’s assessments.

Limitations and Best Judgment: Users should be made aware of the model’s limitations and encouraged to exercise their best judgment, especially for high-stakes decisions that could impact customer safety or financial outcomes.

Error Rate Monitoring: For claims involving potential fraud or high-risk incidents, the error rate of the AI models should be closely monitored, with mechanisms in place to alert users when the error rate exceeds acceptable thresholds.