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

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. 1.Decision Coefficients: Linear weights showing how each feature influences each decision type
2. 2.Top Factors: The 3 most influential features for each specific prediction
3. 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. 1.Synthetic Training Data: Model trained on synthetic data, not real claims
2. 2.Class Imbalance: Reject class has limited examples (5% of data)
3. 3.Feature Coverage: Does not consider driver history, location, or policy details
4. 4.Temporal Factors: No consideration of claim timing or seasonal patterns
5. 5.Fraud Detection: Not designed to detect fraudulent claims
6. 6.Currency: Assumes USD, no currency conversion
7. 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. 1.Track prediction distribution (approve/review/reject ratios)
2. 2.Monitor confidence score distributions
3. 3.Collect human override data for model retraining
4. 4.Review logs monthly for drift detection
5. 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}
}