Unlike collective models, individual models have the advantage of keeping the attributes of each claim intact. We propose a three-component parametric individual model that uses this information in the form of explanatory variables. The first component predicts the delays between the occurrence, report, and closure of each claim using parametric survival models. For the second (frequency) and third (severity) components, we use generalized linear models and splice models. Moreover, the elapsed time between report and closure of claims is converted into an exposure variable in the count model. Finally, we discuss estimation procedures, make predictions, and compare the results with other models using a data set from a major Canadian insurance company.

与集体模型不同，单个模型的优点是保持每个索赔的属性不变。我们提出了一个由三部分组成的参数化个体模型，该模型以解释变量的形式使用此信息。第一部分使用参数生存模型预测每个索赔的发生，报告和结案之间的延迟。对于第二个（频率）和第三个（严重性）分量，我们使用广义线性模型和拼接模型。此外，在计数模型中，从报告到结束索赔之间所经过的时间被转换为风险暴露变量。最后，我们使用加拿大一家主要保险公司的数据集讨论估计程序，进行预测，并将结果与​​其他模型进行比较。