docs/01_CAPABILITY_DICTIONARY.md

# 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.

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## 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.

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## 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.

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## 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.

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## 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?"

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## 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.

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## 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.

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## 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.

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## 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.

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## 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.

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## 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.

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## 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.

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## 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.

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## 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.

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## 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.

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## 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](02_CAPABILITY_MAP.md)** to understand which capabilities power which systems
- Review **[Governance Standards](03_GOVERNANCE.md)** to understand how capabilities are governed
- Consult **[Extension Guide](04_EXTENSION_GUIDE.md)** before adding new capabilities

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**BDR Agent Factory** — The authoritative capability registry for Bader AI.