Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/311245 
Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] European Actuarial Journal [ISSN:] 2190-9741 [Volume:] 13 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 55-90
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor legal insurance claims that the new approach provides more accurate predictions in case of non-homogeneous portfolios.
Schlagwörter: 
Loss reserving
Individual claim features
General insurance
Randomly truncated data
Expectation maximization algorithm
Mixture distribution
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