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AI Capability Dictionary (A–N)
This document provides the complete catalog of AI capabilities used across the Bader AI platform. Each capability is categorized, defined, and contextualized for insurance decision intelligence.
Category A — Natural Language Processing (NLP)
Text Classification
Definition: Categorizing text into predefined classes or labels.
Insurance Relevance: Classify claim descriptions, policy documents, customer inquiries by type, urgency, or risk level.
Named Entity Recognition (NER)
Definition: Identifying and extracting entities (names, dates, locations, amounts) from unstructured text.
Insurance Relevance: Extract claimant names, incident dates, locations, and monetary amounts from claim forms and reports.
Sentiment Analysis
Definition: Determining the emotional tone or sentiment expressed in text.
Insurance Relevance: Analyze customer feedback, complaint letters, or social media mentions to gauge satisfaction and identify escalation risks.
Text Summarization
Definition: Generating concise summaries of longer documents.
Insurance Relevance: Summarize lengthy claim reports, policy documents, or investigation notes for quick executive review.
Question Answering
Definition: Providing direct answers to questions based on a given context or knowledge base.
Insurance Relevance: Answer policyholder questions about coverage, exclusions, or claim status using policy documents as context.
Language Translation
Definition: Converting text from one language to another.
Insurance Relevance: Translate claim documents, policy terms, or customer communications across Arabic, English, and other GCC languages.
Category B — Computer Vision
Image Classification
Definition: Categorizing images into predefined classes.
Insurance Relevance: Classify damage photos (vehicle, property) by severity or type (e.g., minor dent, total loss).
Object Detection
Definition: Identifying and locating objects within images.
Insurance Relevance: Detect vehicles, property damage, or specific items in claim photos to verify incident details.
Image Segmentation
Definition: Partitioning an image into multiple segments or regions.
Insurance Relevance: Segment damaged areas in property or vehicle images to assess repair scope.
Optical Character Recognition (OCR)
Definition: Extracting text from images or scanned documents.
Insurance Relevance: Digitize handwritten claim forms, invoices, medical reports, or ID documents for automated processing.
Facial Recognition
Definition: Identifying or verifying individuals based on facial features.
Insurance Relevance: Verify claimant identity during video claims or detect duplicate claims from the same individual.
Category C — Audio & Speech
Speech Recognition
Definition: Converting spoken language into text.
Insurance Relevance: Transcribe customer service calls, claim interviews, or voice-based claim submissions.
Text-to-Speech (TTS)
Definition: Converting text into spoken audio.
Insurance Relevance: Provide voice-based policy summaries, claim status updates, or accessibility features for visually impaired users.
Speaker Identification
Definition: Identifying who is speaking in an audio recording.
Insurance Relevance: Verify caller identity in phone-based claims or detect fraudulent impersonation attempts.
Audio Classification
Definition: Categorizing audio clips by type or content.
Insurance Relevance: Classify call center recordings by topic (claim inquiry, complaint, policy question) for routing and analysis.
Category D — Multimodal AI
Vision-Language Models
Definition: Models that process both images and text to understand and generate content.
Insurance Relevance: Analyze claim photos alongside written descriptions to verify consistency and detect discrepancies.
Document Understanding
Definition: Extracting structured information from complex documents (forms, invoices, contracts).
Insurance Relevance: Parse insurance claim forms, medical bills, repair invoices, and policy contracts for automated data entry.
Visual Question Answering
Definition: Answering questions about the content of an image.
Insurance Relevance: Answer questions like "Is the damage visible in this photo?" or "What type of vehicle is shown?"
Category E — Generative AI
Text Generation
Definition: Creating human-like text based on prompts or context.
Insurance Relevance: Generate claim summaries, policy explanations, customer communications, or investigation reports.
Image Generation
Definition: Creating images from text descriptions or other inputs.
Insurance Relevance: Generate visual aids for policy explanations or training materials (limited use in production decisions).
Code Generation
Definition: Automatically generating code from natural language descriptions.
Insurance Relevance: Automate report generation scripts, data transformation pipelines, or decision logic implementations.
Category F — Retrieval & Search
Semantic Search
Definition: Finding information based on meaning rather than exact keyword matches.
Insurance Relevance: Search policy documents, claim histories, or knowledge bases using natural language queries.
Retrieval-Augmented Generation (RAG)
Definition: Combining retrieval of relevant documents with generative AI to produce informed responses.
Insurance Relevance: Answer policy questions by retrieving relevant clauses and generating contextual explanations.
Document Retrieval
Definition: Finding relevant documents from a large corpus based on a query.
Insurance Relevance: Retrieve similar past claims, precedent cases, or relevant policy sections for decision support.
Category G — Data, Analytics & Visualization
Anomaly Detection
Definition: Identifying unusual patterns or outliers in data.
Insurance Relevance: Detect fraudulent claims, unusual claim patterns, or data entry errors.
Time Series Analysis
Definition: Analyzing data points collected over time to identify trends or patterns.
Insurance Relevance: Forecast claim volumes, detect seasonal fraud patterns, or predict policy renewals.
Risk Scoring
Definition: Assigning numerical risk scores based on multiple factors.
Insurance Relevance: Score claims, policies, or customers by fraud risk, underwriting risk, or churn probability.
Financial Analysis
Definition: Analyzing financial data to derive insights or forecasts.
Insurance Relevance: Assess claim reserve adequacy, policy profitability, or loss ratios.
Data Visualization
Definition: Creating visual representations of data (charts, graphs, dashboards).
Insurance Relevance: Visualize claim trends, fraud patterns, risk distributions, or portfolio performance.
Category H — Tabular & Structured Data
Tabular Classification
Definition: Classifying rows in structured datasets.
Insurance Relevance: Classify policies by risk tier, claims by approval likelihood, or customers by segment.
Tabular Regression
Definition: Predicting continuous values from structured data.
Insurance Relevance: Predict claim amounts, policy premiums, or customer lifetime value.
Feature Engineering
Definition: Creating new features from raw data to improve model performance.
Insurance Relevance: Derive features like claim frequency, average claim size, or time since last claim for risk models.
Category I — Models, Benchmarks & Evaluation
Model Evaluation
Definition: Assessing model performance using metrics (accuracy, precision, recall, F1, AUC).
Insurance Relevance: Validate fraud detection models, claim approval models, or risk scoring models before deployment.
Explainability (XAI)
Definition: Providing human-understandable explanations for model predictions.
Insurance Relevance: Explain why a claim was flagged for fraud, why a policy was rated high-risk, or why a decision was made.
Bias Detection
Definition: Identifying unfair biases in model predictions.
Insurance Relevance: Ensure claim decisions, underwriting, and fraud detection do not discriminate based on protected attributes.
Model Monitoring
Definition: Tracking model performance over time to detect drift or degradation.
Insurance Relevance: Monitor fraud detection accuracy, claim approval rates, or risk score distributions for drift.
Category J — Recommendation & Decision Systems
Recommendation Systems
Definition: Suggesting items, actions, or content based on user preferences or context.
Insurance Relevance: Recommend policy add-ons, coverage adjustments, or next-best actions for claims handlers.
Decision Support Systems
Definition: Providing data-driven recommendations to assist human decision-making.
Insurance Relevance: Recommend claim approval/rejection, investigation priority, or settlement amounts with supporting evidence.
Scenario Simulation
Definition: Modeling hypothetical scenarios to predict outcomes.
Insurance Relevance: Simulate fraud scenarios, catastrophe impacts, or policy portfolio changes to assess risk.
Category K — Reinforcement Learning
Policy Optimization
Definition: Learning optimal strategies through trial and error.
Insurance Relevance: Optimize claim routing, fraud investigation prioritization, or customer engagement strategies.
Multi-Armed Bandits
Definition: Balancing exploration and exploitation to maximize rewards.
Insurance Relevance: Optimize A/B testing for claim workflows, pricing strategies, or customer communications.
Category L — Knowledge Graphs & Reasoning
Knowledge Graph Construction
Definition: Building structured representations of entities and relationships.
Insurance Relevance: Map relationships between claimants, policies, incidents, and providers to detect fraud rings.
Logical Reasoning
Definition: Applying rules and logic to derive conclusions.
Insurance Relevance: Apply policy rules, coverage conditions, and exclusions to determine claim eligibility.
Ontology Alignment
Definition: Mapping concepts across different knowledge systems.
Insurance Relevance: Align internal policy terms with regulatory definitions or industry standards.
Category M — Automation & Workflow
Robotic Process Automation (RPA)
Definition: Automating repetitive, rule-based tasks.
Insurance Relevance: Automate data entry, document routing, or status updates in claim processing.
Workflow Orchestration
Definition: Coordinating multi-step processes across systems and agents.
Insurance Relevance: Orchestrate claim intake, validation, investigation, approval, and payment workflows.
Task Scheduling
Definition: Automatically scheduling tasks based on priorities and dependencies.
Insurance Relevance: Schedule claim reviews, fraud investigations, or policy renewals based on urgency and capacity.
Category N — Security, Governance & Compliance
Audit Logging
Definition: Recording all system actions and decisions for accountability.
Insurance Relevance: Log every claim decision, model prediction, and user action for regulatory audits.
Access Control
Definition: Managing who can access what data or perform what actions.
Insurance Relevance: Restrict access to sensitive claim data, PII, or financial information based on roles.
Data Privacy (PII Handling)
Definition: Protecting personally identifiable information.
Insurance Relevance: Anonymize, encrypt, or redact PII in claim documents, customer records, and analytics.
Drift Monitoring
Definition: Detecting changes in data distributions or model behavior over time.
Insurance Relevance: Detect shifts in claim patterns, fraud tactics, or customer behavior that may degrade model performance.
Bias Monitoring
Definition: Continuously checking for unfair biases in model outputs.
Insurance Relevance: Ensure ongoing fairness in claim approvals, underwriting, and fraud detection.
Regulatory Compliance
Definition: Ensuring systems meet legal and regulatory requirements.
Insurance Relevance: Comply with IFRS, AML, GDPR, and GCC insurance regulations in all decision systems.
Summary
This dictionary contains 60+ AI capabilities across 14 categories (A–N). Each capability is:
- Defined clearly
- Contextualized for insurance use cases
- Reusable across multiple systems
- Governed by unified standards
Next Steps:
- See Capability Map to understand which capabilities power which systems
- Review Governance Standards to understand how capabilities are governed
- Consult Extension Guide before adding new capabilities
BDR Agent Factory — The authoritative capability registry for Bader AI.