Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

Artificial Neural Networks in Insurance Loss Reserving

Authors
Peter Mulquiney1
1Taylor Fry Consulting Actuaries, Sydney, Australia
Corresponding Author
Peter Mulquiney
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.67How to use a DOI?
Keywords
Insurance, Loss Reserving, Artificial Neural Networks
Abstract

In this paper we analyse insurance data using Artificial Neural Networks (ANN)[1]. In particular, we use ANN for the problem of Loss Reserving. Loss reserving is the practice of estimating the future payments for the claims which have occurred on an insurance portfolio. A difficulty in forecasting future payments is that the time series of payments often depends on influences that are not observable in the historical data. For example, claims cost inflation may depend on future events such as legislative change and changes in judicial attitudes. Because of this, it is often necessary to supplement ANNs with separate forecasts which account for the expected changes in the future claims environment.

Copyright
© 2006, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
Series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2006.67How to use a DOI?
Copyright
© 2006, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Peter Mulquiney
PY  - 2006/10
DA  - 2006/10
TI  - Artificial Neural Networks in Insurance Loss Reserving
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.67
DO  - https://doi.org/10.2991/jcis.2006.67
ID  - Mulquiney2006/10
ER  -