A data driven binning strategy for the construction of insurance tariff classes

Roel Henckaerts, Katrien Antonio, Maxime Clijsters and Roel Verbelen

No 583471, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

Abstract: We present a fully data driven strategy to incorporate continuous risk factors and geographical information in an insurance tariff. A framework is developed that aligns exibility with the practical requirements of an insurance company, its policyholders and the regulator. Our strategy is illustrated with an example from property and casualty (P&C) insurance, namely a motor insurance case study. We start by fitting generalized additive models (GAMs) to the number of reported claims and their corresponding severity. These models allow for flexible statistical modeling in the presence of different types of risk factors: categorical, continuous and spatial risk factors. The goal is to bin the continuous and spatial risk factors such that categorical risk factors result which capture the effect of the covariate on the response in an accurate way, while being easy to use in a generalized linear model (GLM). This is in line with the requirement of an insurance company to construct a practical and interpretable tariff that can be explained easily to stakeholders. We propose to bin the spatial risk factor using Fisher's natural breaks algorithm and the continuous risk factors using evolutionary trees. GLMs are fitted to the claims data with the resulting categorical risk factors. We find that the resulting GLMs approximate the original GAMs closely, and lead to a very similar premium structure.

Keywords: P&C insurance pricing; fequency; severity; continuous risk factors; spatial risk factor; data driven binning; generalized additive models (GAMs); Fisher's natural breaks; evolutionary trees; generalized linear models (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-dcm and nep-ias
References: Add references at CitEc
Citations:

Published in FEB Research report AFI_17115 pages:1-28

Downloads: (external link)
https://lirias.kuleuven.be/bitstream/123456789/583471/1/AFI_17115.pdf (application/pdf)

Related works:
Journal Article: A data driven binning strategy for the construction of insurance tariff classes (2018)
Working Paper: A data driven binning strategy for the construction of insurance tariff classes (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ete:kbiper:583471

Access Statistics for this paper

More papers in Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven
Bibliographic data for series maintained by library EBIB ().