Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315860 
Year of Publication: 
2024
Citation: 
[Journal:] European Actuarial Journal [ISSN:] 2190-9741 [Volume:] 14 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2024 [Pages:] 623-655
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
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.
Subjects: 
Loss reserving
Individual claim features
General insurance
Claim frequency
Claim intensity
Risk modelling
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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