> **Mandatory Governance Disclaimer** > > This system provides non-binding advisory signals only. It does not approve, reject, adjudicate, or execute decisions. All decisions, interpretations, and authority remain exclusively with qualified human professionals. # Model Card: Insurance Claims Decision Support System **Model Version**: 1.0.0 **Last Updated**: 2026-01-04 **Model Type**: Classical Machine Learning (XGBoost Classifier) **Governance Status**: ADVISORY ONLY - Human-in-the-Loop Required --- ## Model Description ### Overview This model is a **classical machine learning classifier** designed to provide **advisory suggestions** for insurance claim severity assessment. It uses XGBoost (gradient boosting decision trees) to analyze claim characteristics and suggest severity levels. **CRITICAL: This is NOT an autonomous decision-making system.** All outputs are advisory suggestions that require mandatory human review and confirmation. ### Architecture - **Algorithm**: XGBoost Classifier (tree-based gradient boosting) - **Type**: Classical ML (NOT neural networks, NOT deep learning, NOT LLMs) - **Training**: Supervised learning on synthetic insurance claims data - **Output**: Three-class classification (Low/Medium/High severity) with confidence scores ### Model Characteristics - **Deterministic**: Same inputs always produce same outputs - **Explainable**: Feature importance and rule signals provided for every prediction - **Transparent**: All decision logic is open source and auditable - **Non-autonomous**: Cannot make binding decisions without human confirmation --- ## Intended Use ### Primary Use Cases ✅ **Educational demonstration** of AI governance principles ✅ **Proof-of-concept** for governed decision support systems ✅ **Training tool** for insurance professionals learning about AI assistance ✅ **Research platform** for studying human-in-the-loop AI systems ✅ **Compliance review** demonstrations for regulatory stakeholders ### Target Audience - AI governance researchers and practitioners - Insurance industry evaluators and trainers - Regulatory compliance officers - Responsible AI designers - Educational institutions ### Appropriate Contexts - Demonstration environments with synthetic data - Educational workshops and training sessions - Prototype testing for governance frameworks - Academic research on AI decision support --- ## Non-Intended Use ### ❌ DO NOT USE FOR: - **Production insurance claims processing** - This is a demonstration system only - **Real financial decisions** - Not validated for real-world claims - **Autonomous decision-making** - Human oversight is mandatory - **Processing real customer data** - Designed for synthetic data only - **Regulatory compliance** without human review - No regulatory approval obtained - **Replacing human insurance adjusters** - Designed to assist, not replace - **High-stakes decisions** without expert review - **Any application** where model errors could cause harm ### Why These Uses Are Prohibited 1. **No Real-World Validation**: Trained only on synthetic data 2. **No Regulatory Approval**: Not certified for insurance operations 3. **Simplified Rules**: Real insurance claims are far more complex 4. **Demonstration Quality**: Built for education, not production 5. **No Liability Coverage**: No guarantees or warranties provided --- ## Training Data ### Dataset Information - **Source**: BDR-AI/insurance_decision_boundaries_v1 (Hugging Face Datasets) - **Type**: Synthetic/demonstration data - **Purpose**: Educational model training only - **Size**: [Varies - check model_metadata.json for specific training run] ### Data Characteristics - **Features**: 4 input features (claim_type, damage_amount, injury_involved, risk_factor) - **Target**: 3 severity levels (Low, Medium, High) - **Distribution**: Balanced across severity classes - **Quality**: Synthetic data generated based on simplified rules ### Data Limitations ⚠ **NOT REAL-WORLD DATA**: This dataset is synthetic and does not represent actual insurance claims ⚠ **SIMPLIFIED**: Real insurance claims involve hundreds of factors, not just 4 ⚠ **NO BIAS TESTING**: Synthetic data may not reflect real-world demographic patterns ⚠ **FROZEN BOUNDARIES**: Decision thresholds are fixed and may not match real insurance practices --- ## Model Performance ### Evaluation Metrics Performance metrics are available in `evaluation_report.json` after running `evaluate.py`. **Typical Performance** (on synthetic test data): - **Accuracy**: ~85-95% (varies by training run) - **Precision/Recall**: Balanced across severity classes - **Confidence Calibration**: Assessed via log loss metric - **Uncertainty Quantification**: Entropy-based uncertainty scores provided ### Performance Interpretation ✓ **High accuracy on synthetic data** - Model learns the simplified rules effectively ⚠ **Unknown real-world performance** - Not tested on actual insurance claims ⚠ **Overconfidence risk** - Synthetic data may lead to higher confidence than warranted ### Confidence Scores - Model provides confidence scores (0.0-1.0) for each prediction - Higher confidence does NOT eliminate need for human review - Low confidence predictions require extra scrutiny - Uncertainty quantification helps prioritize human attention --- ## Limitations ### Technical Limitations 1. **Simplified Feature Set**: Only 4 input features (real claims need many more) 2. **Synthetic Training Data**: Not validated on real insurance claims 3. **Fixed Decision Boundaries**: Cannot adapt to changing insurance standards 4. **No Contextual Understanding**: Cannot consider claim narratives or special circumstances 5. **Limited Claim Types**: Only handles 4 predefined claim types 6. **No Temporal Factors**: Doesn't account for claim timing or seasonal patterns ### Governance Limitations 1. **No Autonomous Operation**: Must have human oversight for every prediction 2. **No Binding Authority**: All outputs are advisory suggestions only 3. **No Regulatory Approval**: Not certified by insurance regulators 4. **Demonstration Quality**: Not built to production standards 5. **No Safety Guarantees**: Errors and mistakes are expected ### Ethical Limitations 1. **Bias Unknown**: Not tested for fairness across demographic groups 2. **Explainability Gaps**: Feature importance doesn't capture all reasoning 3. **No Accountability**: Model cannot be held responsible for decisions 4. **Limited Transparency**: Internal tree structure can be complex 5. **No Appeal Process**: No mechanism for disputing model suggestions ### Operational Limitations 1. **Single Model**: No ensemble or backup systems 2. **No Online Learning**: Cannot improve from new data without retraining 3. **No A/B Testing**: Not designed for production experimentation 4. **Limited Monitoring**: Basic evaluation only, no production monitoring 5. **No SLA Guarantees**: Performance and availability not guaranteed --- ## Human-in-the-Loop Requirements ### MANDATORY Human Oversight 🔴 **CRITICAL**: This system CANNOT and MUST NOT operate without human supervision. ### Human Responsibilities 1. **Review Every Prediction**: Human must independently evaluate each claim 2. **Exercise Independent Judgment**: Do not blindly accept model suggestions 3. **Confirm or Override**: Human decides whether to accept or reject advisory 4. **Document Rationale**: Human must explain reasoning for final decision 5. **Maintain Audit Trail**: All decisions and rationales must be logged ### Enforcement Mechanisms - System outputs clearly marked as "ADVISORY ONLY" - No automatic actions taken based on model predictions - Human confirmation required before any decision is finalized - Override capability provided without restrictions - All human decisions logged with timestamps and rationale ### Human Authority ✅ Human decision-maker has **FULL AUTHORITY** to: - Accept model suggestions - Override model suggestions - Request additional information - Escalate complex cases - Apply contextual judgment The model is a **tool to assist humans**, not a replacement for human expertise. --- ## Explainability and Transparency ### Explainability Features 1. **Feature Importance**: Shows which factors influenced each prediction 2. **Rule Signals**: Human-readable explanation of triggered decision rules 3. **Confidence Scores**: Quantifies model certainty for each prediction 4. **Uncertainty Assessment**: Identifies predictions requiring extra scrutiny 5. **Decision Boundaries**: Fixed thresholds documented and transparent ### Transparency Measures - All code is open source and reviewable - Decision logic based on documented rules (decision_spec.yaml) - Model architecture is classical ML (not black-box deep learning) - Training process fully documented - Evaluation metrics publicly available ### Limitations of Explainability - Feature importance is global, not always case-specific - Tree ensemble decisions can be complex to trace - Interactions between features may not be obvious - Confidence scores can be miscalibrated - Uncertainty measures are estimates, not guarantees --- ## Ethical Considerations ### Transparency Commitment ✓ **No Hidden Logic**: All decision rules are documented and accessible ✓ **Explicit Uncertainty**: Model communicates when it's uncertain ✓ **Human Authority**: Human judgment is preserved and required ✓ **Open Source**: Code and methodology are publicly reviewable ### Accountability Framework ✓ **Human Decision-Maker**: Identified in audit trail for every decision ✓ **Rationale Required**: Human must document reasoning ✓ **Clear Ownership**: Human owns the decision, not the model ✓ **Audit Trail**: Complete record of all decisions maintained ### Safety Measures ✓ **No Autonomous Operation**: System cannot act independently ✓ **Fail-Safe Defaults**: Errors result in human review, not automatic rejection ✓ **Explicit Constraints**: System capabilities clearly bounded ✓ **Override Always Available**: Human can always override suggestions ### Fairness Considerations ⚠ **Bias Testing Not Performed**: Model not evaluated for demographic fairness ⚠ **Synthetic Data Only**: May not reflect real-world population distributions ⚠ **Simplified Features**: May miss important fairness-relevant factors ⚠ **Human Bias Possible**: Human decision-maker may introduce biases **Recommendation**: Any deployment should include fairness auditing and bias testing appropriate to the specific use case. --- ## Technical Specifications ### Environment Requirements - **Python Version**: 3.11 or higher - **Dependencies**: See requirements.txt - scikit-learn >= 1.3.0 - xgboost >= 2.0.0 - pandas >= 2.0.0 - numpy >= 1.24.0 - shap >= 0.42.0 - joblib >= 1.3.0 ### Model Artifacts - **Model File**: model.pkl (joblib serialized XGBoost model) - **Encoders**: encoders.pkl (label encoders for categorical features) - **Metadata**: model_metadata.json (training information and metrics) - **Configuration**: decision_spec.yaml (frozen decision boundaries) ### Input Specification ```python { 'claim_type': str, # "Auto", "Property", "Health", or "Liability" 'damage_amount': float, # USD amount (non-negative) 'injury_involved': bool, # True or False 'risk_factor': str # "low", "medium", or "high" } ``` ### Output Specification ```python { 'model_suggestion': str, # e.g., "High Severity (Advisory)" 'confidence_score': float, # 0.0 to 1.0 'feature_importance': dict, # Feature contributions 'rule_signals': list, # Human-readable explanations 'uncertainty_assessment': dict, # Uncertainty level and metrics 'governance_status': str, # "ADVISORY ONLY" 'requires_human_review': bool # Always True } ``` ### Usage Example ```python from predict import predict_claim result = predict_claim( claim_type="Auto", damage_amount=15000.0, injury_involved=True, risk_factor="medium" ) print(f"Advisory Suggestion: {result['model_suggestion']}") print(f"Confidence: {result['confidence_score']:.2%}") print(f"Human Review Required: {result['requires_human_review']}") ``` --- ## Maintenance and Updates ### Version History - **v1.0.0** (2026-01-04): Initial release - XGBoost classifier trained on synthetic dataset - Advisory-only governance framework - Human-in-the-loop enforcement - Feature importance and uncertainty quantification ### Update Policy - Model frozen for demonstration purposes - Retraining requires explicit approval - Decision boundaries cannot be modified - Governance constraints are immutable ### Contact and Support This is a demonstration model for the BDR Agent Factory governance framework. For questions about governance principles or implementation: - Review the decision_spec.yaml file - Consult the QODER_EXECUTION_BRIEF.md - Refer to project documentation --- ## Governance Compliance Summary ### ✅ Compliance Verified - [x] Classical ML only (no LLMs, no neural networks) - [x] Advisory-only outputs (no autonomous decisions) - [x] Human review required for all predictions - [x] Only allowed features used (4 features as specified) - [x] Decision boundaries documented and frozen - [x] Explainability artifacts generated - [x] Uncertainty quantification provided - [x] Audit trail support implemented - [x] Override capability enabled - [x] Limitations clearly documented ### Governance Framework This model operates under the **BDR Agent Factory** governance framework: - **No autonomous actions**: System cannot take actions without human approval - **Transparency**: All logic is explainable and auditable - **Human authority**: Human has final decision-making power - **Accountability**: Human decision-maker is logged and responsible - **Safety**: System designed with fail-safe constraints --- ## License and Disclaimer ### License This model and associated code are provided for educational and research purposes. Suggested License: Apache 2.0 or MIT (specify as appropriate for your use case) ### Disclaimer **THIS MODEL IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED.** ⚠ **IMPORTANT DISCLAIMERS**: 1. **No Production Use**: This model is for demonstration and education only 2. **No Accuracy Guarantees**: Performance on real-world data is unknown 3. **No Regulatory Approval**: Not certified for insurance operations 4. **No Liability Coverage**: Use at your own risk 5. **Human Oversight Required**: Must not operate autonomously 6. **Synthetic Data Only**: Not validated on real insurance claims 7. **Educational Purpose**: Designed for learning, not production deployment ### Responsible Use Users of this model are responsible for: - Ensuring appropriate human oversight - Complying with applicable regulations - Conducting their own validation and testing - Not deploying in high-stakes scenarios without proper safeguards - Maintaining audit trails and accountability --- ## Conclusion This model demonstrates how classical machine learning can be deployed under strict governance constraints to provide **advisory decision support** while preserving human authority and accountability. **Key Takeaways**: ✓ Advisory suggestions, not autonomous decisions ✓ Human-in-the-loop is mandatory ✓ Transparency and explainability built-in ✓ Clear documentation of limitations ✓ Designed for education, not production **Remember**: This is a tool to **assist humans**, not replace them. The final decision authority always rests with qualified human professionals. --- **Model Card Version**: 1.0.0 **Last Reviewed**: 2026-01-04 **Next Review**: Required before any production consideration (not currently approved)