The projection of outstanding liabilities caused by incurred losses or claims has played a fundamental role in general insurance operations. Loss reserving methods based on individual losses generally perform better than those based on aggregate losses. This study uses a parametric individual information model taking not only individual losses but also individual information such as age, gender, and so on from policies themselves into account. Based on this model, this study proposes a computation procedure for the projection of the outstanding liabilities, discusses the estimation and statistical properties of the unknown parameters, and explores the asymptotic behaviors of the resulting loss reserving as the portfolio size approaching infinity. Most importantly, this study demonstrates the benefits of individual information on loss reserving. Remarkably, the accuracy gained from individual information is much greater than that from considering individual losses. Therefore, it is highly recommended to use individual information in loss reserving in general insurance.

由发生的损失或索赔引起的未偿债务的预测在一般保险业务中发挥了根本作用。基于个人损失的损失准备金方法通常要优于基于总损失的损失准备金方法。本研究使用参数化的个人信息模型，该模型不仅考虑了个人损失，而且还考虑了政策本身中的诸如年龄，性别等个人信息。在此模型的基础上，本研究提出了一种计算未偿债务的计算程序，讨论了未知参数的估计和统计性质，并探讨了随着投资组合规模趋于无穷大而导致的亏损保留的渐近行为。最重要的是，这项研究证明了个人信息在保留损失方面的好处。值得注意的是，从个体信息中获得的准确性要比考虑个体损失时的准确性高得多。因此，强烈建议在一般保险的损失准备金中使用个人信息。