Claims AI Glossary
Key terms and definitions for FNOL automation and AI-powered claims processing
Clear definitions to help you understand AI for insurance claims — from FNOL automation to explainable AI.
FNOL (First Notice of Loss)
The initial report of a claim to an insurance carrier. FNOL is the first point of contact when a policyholder reports an incident, accident, or loss. It typically includes basic information about what happened, when, where, and who was involved.
In Practice: FNOL is where most manual effort occurs in claims processing. Automating FNOL intake can reduce processing time by 40-60% and improve data quality significantly.
Claims Intake Automation
The use of AI and automation technology to capture, validate, and structure claim information from policyholders without manual data entry. This includes extracting data from forms, emails, phone calls, and documents.
In Practice: Automated claims intake transforms unstructured inputs (like phone calls or emails) into clean, structured data that can be immediately used for decision-making and routing.
Structured Data
Information organized in a predefined format that can be easily searched, analyzed, and processed by systems. In claims, this means converting free-form text or verbal descriptions into standardized fields and values.
In Practice: Structured FNOL data enables downstream automation, better routing decisions, and more accurate risk assessment.
Document Intelligence
AI technology that extracts, classifies, and validates information from documents such as police reports, medical records, repair estimates, and photos. Goes beyond simple OCR to understand context and meaning.
In Practice: Document intelligence extends FNOL automation by processing supporting documents automatically, reducing manual review time by 50-70%.
Claims Triage
The process of evaluating and routing claims based on complexity, severity, and required expertise. AI-powered triage uses structured data to make consistent routing decisions.
In Practice: Automated triage ensures claims reach the right adjuster faster, reducing assignment time from hours to minutes.
Explainable AI (XAI)
AI systems designed to provide clear reasoning for their decisions and recommendations. In insurance, this means showing why a claim was routed a certain way or what data points led to a risk assessment.
In Practice: Explainable AI is critical for regulatory compliance and building trust with adjusters who need to understand and validate AI decisions.
AI Governance
The framework of policies, controls, and oversight mechanisms that ensure AI systems operate safely, fairly, and in compliance with regulations. Includes monitoring, auditability, and human oversight.
In Practice: Strong AI governance enables insurers to deploy AI with confidence, knowing decisions are traceable and controllable.
Low-Risk Pilot
A controlled deployment of AI technology on a subset of claims or workflows to validate performance before full-scale implementation. Designed to prove value without disrupting existing operations.
In Practice: Pilots typically run 4-6 weeks and focus on one specific use case like FNOL automation, allowing insurers to measure results before committing to broader deployment.
Claims Workflow
The series of steps and processes a claim goes through from first notice to final settlement. Includes intake, investigation, evaluation, negotiation, and payment.
In Practice: AI can be deployed at specific points in the workflow (like FNOL) without requiring changes to the entire process.
Data Quality
The accuracy, completeness, consistency, and reliability of information captured during claims processing. Poor data quality leads to delays, errors, and inefficient decision-making.
In Practice: AI-powered FNOL automation typically improves data quality from 70-80% accuracy to 95%+ by validating inputs in real-time.
Audit Trail
A complete, chronological record of all actions, decisions, and data changes in a claims process. Essential for regulatory compliance and quality assurance.
In Practice: AI systems must maintain detailed audit trails showing what data was used, what decisions were made, and what confidence scores were assigned.
Confidence Score
A numerical value (typically 0-100%) indicating how certain an AI system is about a prediction or decision. Low confidence scores trigger human review.
In Practice: Confidence scores allow adjusters to focus on uncertain cases while trusting high-confidence AI decisions.
Straight-Through Processing (STP)
The ability to process a claim from start to finish without manual intervention. Enabled by high-quality structured data and automated decision-making.
In Practice: FNOL automation is the foundation for STP - without clean intake data, downstream automation isn't possible.
Natural Language Processing (NLP)
AI technology that understands and processes human language from text or speech. Used to extract information from claim descriptions, emails, and phone calls.
In Practice: NLP powers FNOL automation by converting conversational claim reports into structured data fields.
OCR (Optical Character Recognition)
Technology that converts images of text (like scanned documents or photos) into machine-readable text. Foundation for document processing.
In Practice: Modern document intelligence goes beyond OCR to understand context, validate information, and extract structured data from complex documents.
Have questions about these terms?
We're happy to explain how these concepts apply to your specific claims workflows. Get in touch.