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Activity Number:
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87
- Invited ePoster Session: a Statistical Smörgåsbord
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
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Sponsor:
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Section on Risk Analysis
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Abstract #328686
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Title:
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Two Mixture-Based Clustering Approaches: Modeling an Automobile Insurance Portfolio
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Author(s):
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Tatjana Miljkovic* and Daniel Fernandez
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Companies:
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Miami University and Victoria University of Wellington
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Keywords:
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OSM;
ordinal data;
stereotype model;
CWM;
GLM
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Abstract:
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We review two mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio. These approaches include: the generalized linear mixed cluster-weighted model and the mixture-based clustering for ordered stereotype model. Model fitting is performed using the expectation-maximization (EM) algorithm. These approaches are introduced to be used by the practitioners working in financial institutions with interests in insurance and academics specializing in insurance related research. The application of both methods is illustrated on a well-known French automobile portfolio. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management. The application of both methods is illustrated on a well-known French automobile portfolio.
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Authors who are presenting talks have a * after their name.
