Authors:
Jin Zhang
1
;
2
;
Shengkun Xie
1
;
Anna Lawniczak
2
and
Clare Chua-Chow
1
Affiliations:
1
Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, Canada
;
2
Department of Mathematics and Statistics, University of Guelph, Guelph, Canada
Keyword(s):
Spatial Models, Rate-Making, Insurance Analytics, Business Data Analytics.
Abstract:
This study explores the dynamics of automobile insurance claim frequencies, shedding light on spatial patterns indicative of regional diversity. By examining data from urban, rural, and suburban areas, we discern disparate claim frequencies across both geographical and temporal dimensions, offering pivotal insights for insurers and regulators seeking to enhance risk assessment and pricing methodologies. Our analysis of auto insurance loss data from Ontario, Canada, unveils a marked divergence in relative claim frequencies between the expansive northern regions and the densely populated south. Furthermore, by scrutinizing various accident years, including those influenced by the COVID-19 pandemic, distinct temporal trends emerge. Applying sophisticated spatio-temporal models facilitates precise predictions, equipping insurers with the tools necessary for adept navigation of the ever-evolving landscape of uncertainties. This research enhances our comprehension of the dynamic nature of
territory risk within spatio-temporal contexts. These insights provide valuable assistance to insurance companies and auto insurance regulators in effectively managing territorial risk.
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