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Ordered Lorenz Regularization (OLR): A General Method to Mitigate Overfitting in General Insurance Pricing via Machine Learning Algorithms

Author

Listed:
  • Justin Morgan

    (Colorado Technical University, USA)

  • Yanzhen Qu

    (Colorado Technical University, USA)

Abstract

When machine learning algorithms are used to determine the price of general insurance, they can sometimes overfit the data. This overfitting can lead to problems for both customers and insurance companies. To address this issue, we’ve developed a new approach called Ordered Lorenz Regularization (OLR). We have tested OLR on general insurance data. The results have demonstrated that OLR is successful in reducing overfitting. Additionally, when we use OLR for pricing general insurance, it helps establish the lowest and highest possible premiums.

Suggested Citation

  • Justin Morgan & Yanzhen Qu, 2024. "Ordered Lorenz Regularization (OLR): A General Method to Mitigate Overfitting in General Insurance Pricing via Machine Learning Algorithms," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 8(5), pages 6-11, September.
    • Handle: RePEc:epw:ejece0:v:8:y:2024:i:5:id:19646
    DOI: 10.24018/ejece.2024.8.5.646
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