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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 

{
  '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 

{
  '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 

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

  • Classical ML only (no LLMs, no neural networks)
  • Advisory-only outputs (no autonomous decisions)
  • Human review required for all predictions
  • Only allowed features used (4 features as specified)
  • Decision boundaries documented and frozen
  • Explainability artifacts generated
  • Uncertainty quantification provided
  • Audit trail support implemented
  • Override capability enabled
  • 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)

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