bdr-ai-org/insurance-claims-decision-model-v1

Motor Insurance Claims Decision Model

Model Description

This is a Logistic Regression model for motor insurance claims decision support. The model predicts whether a claim should be approved, sent for review, or rejected based on claim characteristics.

Model Type: Logistic Regression (Multinomial)
Version: v1
Framework: scikit-learn
Language: Python 3.9+

Intended Use

Primary Use Case

This model is designed for human-in-the-loop decision support in motor insurance claims processing. It provides:

• A recommended decision (approve/review/reject)
• Confidence score for the recommendation
• Top 3 factors influencing the decision

Intended Users

• Insurance claims adjusters
• Claims processing teams
• Risk assessment specialists

Out-of-Scope Use

❌ This model should NOT be used for:

• Fully automated claims decisions without human review
• Legal or regulatory compliance decisions
• Claims outside motor insurance domain
• High-value claims above $50,000 without additional review
• Fraud detection (not trained for this purpose)

Model Architecture

Algorithm: Logistic Regression with multinomial classification
Solver: lbfgs
Class Balancing: Enabled (class_weight='balanced')

Input Features (10 total)

1. Claim Amount - Dollar amount claimed
2. Vehicle Age - Age of vehicle in years
3. Accident Type - Category (collision, theft, vandalism, weather_damage, fire)
4. Police Report - Whether police report exists (yes/no)
5. Repair Estimate - Estimated repair cost
6. Prior Claims - Number of previous claims
7. Claim to Estimate Ratio - Derived feature
8. High Claim Flag - Binary flag for claims > $15,000
9. Old Vehicle Flag - Binary flag for vehicles > 10 years
10. Multiple Claims Flag - Binary flag for > 2 prior claims

Output Format

{
  "decision": "approve|review|reject",
  "confidence": 0.0-1.0,
  "top_factors": ["factor_1", "factor_2", "factor_3"]
}

Training Data

Dataset: insurance-motor-claims-decision-v1
Source: Synthetic data generated with logical business rules
Size: 800 records (640 training, 160 test)
Split: 80/20 train-test, stratified by decision class

Class Distribution (Training Set)

• Review: ~73% (most common)
• Approve: ~22%
• Reject: ~5%

Performance Metrics

Test Accuracy: 86.4%

Per-Class Performance

Decision	Precision	Recall	F1-Score	Support
Approve	0.86	0.89	0.87	35
Reject	0.44	1.00	0.62	8
Review	0.95	0.71	0.81	117

Macro Average: Precision 0.75, Recall 0.87, F1 0.76
Weighted Average: Precision 0.86, Recall 0.86, F1 0.78

Explainability

This model uses Logistic Regression specifically for its explainability. Each prediction includes:

1. Decision Coefficients: Linear weights showing how each feature influences each decision type
2. Top Factors: The 3 most influential features for each specific prediction
3. Confidence Score: Probability of the predicted class

Key Decision Factors

APPROVE Decision - Influenced by:

• Police Report presence (increases likelihood)
• Lower prior claims count
• Reasonable claim-to-estimate ratio

REJECT Decision - Influenced by:

• High claim flag (claims > $15,000)
• Multiple prior claims
• Large claim-to-estimate ratio discrepancy

REVIEW Decision - Influenced by:

• Moderate claim amounts
• Accident type (certain types trigger review)
• Old vehicle flag

Limitations

Known Limitations

1. Synthetic Training Data: Model trained on synthetic data, not real claims
2. Class Imbalance: Reject class has limited examples (5% of data)
3. Feature Coverage: Does not consider driver history, location, or policy details
4. Temporal Factors: No consideration of claim timing or seasonal patterns
5. Fraud Detection: Not designed to detect fraudulent claims
6. Currency: Assumes USD, no currency conversion
7. Vehicle Types: No distinction between vehicle types (sedan, truck, luxury, etc.)

Performance Limitations

• Reject Class: Lower precision (0.44) due to limited training examples
• Review Class: Lower recall (0.71) - may miss some cases requiring review
• Confidence Calibration: Confidence scores may not be perfectly calibrated

Known Failure Cases

1. Edge Case Claims

Scenario: Claims with unusual combinations (e.g., very old vehicle with very low claim amount)
Impact: Model may provide low-confidence predictions
Mitigation: Always review predictions with confidence < 0.6

2. High-Value Claims

Scenario: Claims exceeding $30,000
Impact: Limited training data in this range may reduce accuracy
Mitigation: Automatically route high-value claims for manual review

3. Missing Police Reports for Theft

Scenario: Theft claims without police reports
Impact: Model may incorrectly approve when rejection is warranted
Mitigation: Implement business rule override for theft + no police report

4. Multiple Prior Claims Edge Cases

Scenario: Customers with 5+ prior claims but legitimate current claim
Impact: May be incorrectly flagged for rejection
Mitigation: Human review required for customers with extensive claim history

5. Claim-to-Estimate Ratio Anomalies

Scenario: Claim amount significantly different from repair estimate
Impact: May trigger incorrect review/reject decisions
Mitigation: Investigate discrepancies before accepting model recommendation

Ethical Considerations

Bias Considerations

• Model does not consider demographic information (age, gender, location)
• Synthetic data may not reflect real-world claim distributions
• Class imbalance may lead to under-representation of reject cases

Fairness

• Model should be monitored for disparate impact across customer segments
• Regular audits recommended to ensure fair treatment
• Human oversight required for all final decisions

Privacy

• Model does not require or use personally identifiable information (PII)
• Input features are claim-specific, not customer-specific
• Logging mechanism should comply with data retention policies

Usage Instructions

Installation

pip install scikit-learn joblib numpy pandas

Loading the Model

from predict import ClaimsDecisionPredictor

predictor = ClaimsDecisionPredictor('model_artifacts')

Making Predictions

claim = {
    "claim_amount": 5000.0,
    "vehicle_age": 5,
    "accident_type": "collision",
    "police_report": "yes",
    "repair_estimate": 4800.0,
    "prior_claims": 1
}

result = predictor.predict(claim)
print(result)
# Output: {"decision": "approve", "confidence": 0.85, "top_factors": [...]}

With Logging

result = predictor.predict_with_logging(claim)

Model Governance

Version Control

• Current Version: v1
• Release Date: January 1, 2026
• Model Hash: Stored in metadata.json

Monitoring Recommendations

1. Track prediction distribution (approve/review/reject ratios)
2. Monitor confidence score distributions
3. Collect human override data for model retraining
4. Review logs monthly for drift detection
5. Retrain quarterly with new data

Update Triggers

• Accuracy drops below 80%
• Significant change in claim patterns
• New business rules introduced
• Regulatory requirement changes

Disclaimer

⚠️ IMPORTANT: This model provides decision support only. All final decisions must be made by qualified human claims adjusters. The model is not a substitute for professional judgment, regulatory compliance, or legal requirements.

This model is provided "as-is" without warranties. Users are responsible for:

• Validating predictions before taking action
• Ensuring compliance with insurance regulations
• Maintaining human oversight of all decisions
• Monitoring for bias and fairness issues

Contact & Support

Model Maintainer: BDR AI Organization
Dataset: insurance-motor-claims-decision-v1
License: MIT (for demonstration purposes)

Citation

@misc{insurance_claims_model_v1,
  title={Motor Insurance Claims Decision Support Model},
  author={BDR AI Organization},
  year={2026},
  publisher={Hugging Face},
  version={v1}
}