# 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](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 --- **BDR Agent Factory** — The authoritative capability registry for Bader AI.