The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine learning techniques. Evaluations focus on mean absolute error (MAE) and root mean squared error (RMSE) metrics to comprehensively assess predictive performance. For frequency prediction, extreme gradient boosting (XGBoost) demonstrates the lowest MAE, indicating higher accuracy compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Despite XGBoost’s lower MAE, it shows higher RMSE values, suggesting a broader error spread and larger magnitudes compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Conversely, the generalized linear model (Poisson) showcases the best RMSE values, indicating tighter clustering and smaller error magnitudes, despite a slightly higher MAE. For severity prediction, extreme gradient boosting (XGBoost) displays the lowest MAE, implying better accuracy. However, it exhibits a higher RMSE, indicating wider error dispersion compared to a generalized linear model (Gamma). In contrast, a generalized linear model (Gamma) demonstrates the lowest RMSE, portraying tighter clustering and smaller error magnitudes despite a higher MAE. In conclusion, extreme gradient boosting (XGBoost) stands out in mean absolute error (MAE) for both frequency and severity prediction, showcasing superior accuracy. However, a generalized linear model (Gamma) offers a balance between accuracy and error magnitude, and its performance outperforms extreme gradient boosting (XGBoost) and gradient boosting machines (GBMs) in terms of RMSE metrics, with a slightly higher MAE. These findings empower insurance companies to enhance risk assessment processes, set suitable premiums, manage reserves, and accurately forecast claim occurrences, contributing to competitive pricing for clients while ensuring profitability. For cross-border trade entities, such as trucking companies and cargo owners, these insights aid in improved risk management and potential cost savings by enabling more reasonable insurance premiums based on accurate predictive claims from insurance companies.

中文翻译：

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用于预测泰国边境贸易背景下货物保险频率和严重程度的广义线性模型和机器学习方法

该研究比较了跨境货物保险领域索赔频率和严重程度预测建模的模型方法。目的是确定广义线性模型 (GLM) 和高级机器学习技术之间的最佳模型方法。评估重点关注平均绝对误差 (MAE) 和均方根误差 (RMSE) 指标，以全面评估预测性能。对于频率预测，极限梯度提升 (XGBoost) 表现出最低的 MAE，表明与梯度提升机 (GBM) 和广义线性模型 (泊松) 相比具有更高的精度。尽管 XGBoost 的 MAE 较低，但它显示出较高的 RMSE 值，这表明与梯度增强机 (GBM) 和广义线性模型 (泊松) 相比，误差范围更广且幅度更大。相反，广义线性模型（泊松）展示了最佳 RMSE 值，表明聚类更紧密，误差幅度更小，尽管 MAE 稍高。对于严重性预测，极端梯度提升 (XGBoost) 显示最低的 MAE，这意味着更高的准确性。然而，它表现出更高的 RMSE，表明与广义线性模型 (Gamma) 相比，误差分散更宽。相比之下，广义线性模型 (Gamma) 表现出最低的 RMSE，尽管 MAE 较高，但聚类更紧密，误差幅度更小。总之，极限梯度提升 (XGBoost) 在频率和严重性预测的平均绝对误差 (MAE) 方面脱颖而出，展现出卓越的准确性。然而，广义线性模型 (Gamma) 在精度和误差幅度之间提供了平衡，其性能在 RMSE 指标方面优于极限梯度提升 (XGBoost) 和梯度提升机 (GBM)，MAE 略高。这些发现使保险公司能够加强风险评估流程、设定适当的保费、管理准备金并准确预测索赔发生情况，从而在确保盈利能力的同时为客户提供有竞争力的定价。对于货运公司和货主等跨境贸易实体来说，这些见解可以根据保险公司准确的预测索赔提供更合理的保险费，从而有助于改善风险管理并节省潜在的成本。