claims-advisory-scoring-model / README.md

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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
	```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)