Soft splicing model: bridging the gap between composite model and finite mixture model

Tsz Chai Fung, Himchan Jeong, George Tzougas

Research output: Contribution to journalArticlepeer-review


Considerations of both the heavy-tail phenomenon and multi-modality of a claim severity distribution have been challenging in the actuarial literature and practices. In this article, we develop a novel class of soft splicing models that bridges the gap between pre-existing methods for handling the issues above. The proposed method is flexible enough to incorporate tail-heaviness and multi-modality with computational efficiency and nests finite mixture models and splicing models as its special and/or limiting cases. The soft splicing model is also more robust in extrapolating the tail-heaviness of distribution subject to model contamination. According to simulation studies and real insurance claim data analyses, it is shown that the proposed soft splicing model provides superior goodness-of-fit and more accurate estimates of tail risk measures than both finite mixture and composite models.
Original languageEnglish
Pages (from-to)168-197
Number of pages30
JournalScandinavian Actuarial Journal
Issue number2
Early online date13 Jul 2023
Publication statusPublished - 7 Feb 2024


  • Claim severity modeling
  • mixture models
  • multi-modal distribution
  • risk management
  • tail-heaviness

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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