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Forecasting and analyzing insurance companies' ratings

Forecasting and analyzing insurance companies' ratings
Forecasting and analyzing insurance companies' ratings
Insurance companies sell protection to policy holders against many types of risks: property damage or loss, health and casualties, financial losses, etc. In return for this risk protection, insurance companies receive a premium from the policy holder, which is used to cover expenses and the expected risk. For longer-term risk protections, part of the premiums are invested to get higher yields. Although the protection buyer mitigates the individual risk to the large and better diversified portfolio of the insurer, it does not mean that the risk is completely reduced since the insurer may default his obligations. Insurers need to have sufficient equity or buffer capital to meet their obligations in adverse conditions when their losses on the diversified portfolio exceed the expected losses. Ratings provide an assessment of the ability of the insurer to meet its obligations to policy holders and debt holders. In this paper, the relationship between financial ratios and the rating is analyzed for different types of insurance companies using advanced statistical techniques that are able to detect non-linear relationship. The resulting rating model approach is similar to the approach for a low default portfolio, which uses a common set of explanatory variables (such as capitalization, profitability, leverage and size) which is generally applicable for all insurance types, and is complemented with insurance type specific ratios. The resulting model is found to yield a good accuracy, with 75% of the model ratings differing at most one notch from the external rating.
credit scoring, internal rating system, insurance companies
0169-2070
513-529
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Martens, D.
cda8c1d8-591a-402b-a8c4-800a02979bd7
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Feremans, D.
94831ea4-50da-47fa-bb06-b538e0c1c333
Huysmans, J.
4926c4a3-4dd3-477f-a352-de2a432f2d61
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Martens, D.
cda8c1d8-591a-402b-a8c4-800a02979bd7
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Feremans, D.
94831ea4-50da-47fa-bb06-b538e0c1c333
Huysmans, J.
4926c4a3-4dd3-477f-a352-de2a432f2d61
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999

Van Gestel, T., Martens, D., Baesens, B., Feremans, D., Huysmans, J. and Vanthienen, J. (2007) Forecasting and analyzing insurance companies' ratings. International Journal of Forecasting, 23 (3), 513-529. (doi:10.1016/j.ijforecast.2007.05.001).

Record type: Article

Abstract

Insurance companies sell protection to policy holders against many types of risks: property damage or loss, health and casualties, financial losses, etc. In return for this risk protection, insurance companies receive a premium from the policy holder, which is used to cover expenses and the expected risk. For longer-term risk protections, part of the premiums are invested to get higher yields. Although the protection buyer mitigates the individual risk to the large and better diversified portfolio of the insurer, it does not mean that the risk is completely reduced since the insurer may default his obligations. Insurers need to have sufficient equity or buffer capital to meet their obligations in adverse conditions when their losses on the diversified portfolio exceed the expected losses. Ratings provide an assessment of the ability of the insurer to meet its obligations to policy holders and debt holders. In this paper, the relationship between financial ratios and the rating is analyzed for different types of insurance companies using advanced statistical techniques that are able to detect non-linear relationship. The resulting rating model approach is similar to the approach for a low default portfolio, which uses a common set of explanatory variables (such as capitalization, profitability, leverage and size) which is generally applicable for all insurance types, and is complemented with insurance type specific ratios. The resulting model is found to yield a good accuracy, with 75% of the model ratings differing at most one notch from the external rating.

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More information

Published date: July 2007
Keywords: credit scoring, internal rating system, insurance companies
Organisations: Management

Identifiers

Local EPrints ID: 51703
URI: http://eprints.soton.ac.uk/id/eprint/51703
ISSN: 0169-2070
PURE UUID: 175a09fd-9e77-49fb-8515-584fd5889bd1
ORCID for B. Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 20 Aug 2008
Last modified: 16 Mar 2024 03:39

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Contributors

Author: T. Van Gestel
Author: D. Martens
Author: B. Baesens ORCID iD
Author: D. Feremans
Author: J. Huysmans
Author: J. Vanthienen

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