Usually, the actuarial problems of predicting the number of claims incurred but not reported (IBNR) and of modelling claim frequencies are treated successively by insurance companies. New micro-level methods designed for large datasets are proposed that address the two problems simultaneously. The methods are based on an elaborated occurrence process model that includes both a claim intensity model and a claim development model. The influence of claim feature variables is modelled by suitable neural networks. Extensive simulation experiments and a case study on a large real data set from a motor legal insurance portfolio show accurate predictions at both the aggregate and individual policy level, as well as appropriate fitted models for claim frequencies. Moreover, a novel alternative approach combining data from classic triangle-based methods with a micro-level intensity model is introduced and compared to the full micro-level approach.

通常，保险公司会相继处理预测已发生但未报告的索赔数量（IBNR）和建模索赔频率的精算问题。提出了针对大型数据集设计的新微观方法，可以同时解决这两个问题。这些方法基于详细的发生过程模型，该模型包括索赔强度模型和索赔发展模型。索赔特征变量的影响通过合适的神经网络建模。对汽车法律保险投资组合中的大量真实数据集进行的广泛模拟实验和案例研究显示了总体和个人保单层面的准确预测，以及索赔频率的适当拟合模型。此外，还引入了一种新颖的替代方法，将经典的基于三角形的方法的数据与微观强度模型相结合，并与完整的微观方法进行了比较。