Jason Bedford
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Data Science for Smarter MVR Ordering: Optimizing Auto Insurance Costs and Risk Assessment

Data Science for Smarter MVR Ordering: Optimizing Auto Insurance Costs and Risk Assessment

Motor Vehicle Reports (MVRs) are crucial for assessing driver risk in auto insurance, but blanket ordering for all policy renewals is expensive. This post explores how data science can optimize MVR ordering, saving money and improving risk assessment. We’ll dive into the predictive modeling process, demonstrating how to identify high-risk policyholders for targeted MVR checks.

Introduction

MVRs provide a comprehensive driving history, essential for underwriting and renewal decisions. However, the cost of obtaining these reports for every policyholder adds up. This post investigates data-driven approaches to MVR ordering, leveraging machine learning to achieve cost efficiency and enhanced risk assessment. We’ll explore how to build a model that predicts the likelihood of an MVR impacting premium calculations, allowing insurers to focus resources on the most relevant cases.

The Predictive Power of MVRs

An MVR offers a detailed snapshot of a driver’s record, including license status, violations, accidents, and more. This information is invaluable for accurate risk assessment, premium calculation, and regulatory compliance. However, indiscriminately ordering MVRs for all renewals represents a significant expense. Our goal is to use data science to pinpoint when an MVR is most likely to reveal valuable information.

Leveraging Machine Learning for Optimized MVR Ordering

Previous research has demonstrated the effectiveness of machine learning in predicting insurance risk factors. We build on this foundation, applying predictive modeling to determine when MVRs are most likely to influence underwriting decisions. This allows for a more targeted approach, maximizing the value of MVR insights while minimizing costs.

Data and Methodology

Our dataset comprises 100,000 auto policyholder records over a five-year period, provided by a major insurance carrier. The data includes features such as age, location, policy type, prior accidents and violations, and whether a renewal MVR was ordered and impacted premiums.

We trained several machine learning classification algorithms on 80% of the data to predict the likelihood of an MVR impacting premiums. The remaining 20% served as a holdout set for evaluating model performance. We employed the following models:

  • Logistic Regression: A foundational linear model for binary classification.
  • Random Forest Classification: An ensemble method combining multiple decision trees for improved accuracy and robustness.
  • Support Vector Machine (SVM): A powerful model effective in high-dimensional spaces, known for its ability to capture complex relationships.

Hyperparameter tuning was performed using randomized search to optimize each model. We evaluated performance using accuracy, AUC-ROC, precision, recall, and F1-score.

Results and Discussion

The SVM model achieved the highest performance, with an accuracy of 81.3% and an AUC-ROC of 0.83 on the test set. Key predictors included age under 25, urban location, and sports car ownership. These findings suggest that focusing MVR checks on policyholders identified as high-risk by the model can significantly improve risk assessment while reducing unnecessary costs.

Conclusion and Future Directions

Data science offers a powerful toolkit for optimizing MVR ordering in auto insurance. By leveraging machine learning, insurers can achieve substantial cost savings while maintaining accurate risk assessment. Future work could explore more advanced techniques, such as deep learning models, to further refine predictions and unlock additional value from MVR data.