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

Author

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  • Justin Morgan
  • Yanzhen Qu

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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    References listed on IDEAS

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    1. Frees, Edward W. & Meyers, Glenn & Cummings, A. David, 2011. "Summarizing Insurance Scores Using a Gini Index," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1085-1098.
    2. Roel Henckaerts & Marie-Pier Côté & Katrien Antonio & Roel Verbelen, 2021. "Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(2), pages 255-285, April.
    3. Liqun Diao & Chengguo Weng, 2019. "Regression Tree Credibility Model," North American Actuarial Journal, Taylor & Francis Journals, vol. 23(2), pages 169-196, April.
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