Artikel
Defining geographical rating territories in auto insurance regulation by spatially constrained clustering
Territory design and analysis using geographical loss cost are a key aspect in auto insurance rate regulation. The major objective of this work is to study the design of geographical rating territories by maximizing the within-group homogeneity, as well as maximizing the among-group heterogeneity from statistical perspectives, while maximizing the actuarial equity of pure premium, as required by insurance regulation. To achieve this goal, the spatially-constrained clustering of industry level loss cost was investigated. Within this study, in order to meet the contiguity, which is a legal requirement on the design of geographical rating territories, a clustering approach based on Delaunay triangulation is proposed. Furthermore, an entropy-based approach was introduced to quantify the homogeneity of clusters, while both the elbow method and the gap statistic are used to determine the initial number of clusters. This study illustrated the usefulness of the spatially-constrained clustering approach in defining geographical rating territories for insurance rate regulation purposes. The significance of this work is to provide a new solution for better designing geographical rating territories. The proposed method can be useful for other demographical data analysis because of the similar nature of the spatial constraint.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Risks ; ISSN: 2227-9091 ; Volume: 7 ; Year: 2019 ; Issue: 2 ; Pages: 1-20 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
rate-making
rating territory
insurance rate filing
spatially-constrained clustering
entropy methods
clustering
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Xie, Shengkun
Esposito, Emilio Xavier
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2019
- DOI
-
doi:10.3390/risks7020042
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Xie, Shengkun
- Esposito, Emilio Xavier
- MDPI
Entstanden
- 2019
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