Premium control with reinforcement learning

We consider a premium control problem in discrete time, formulated in terms of a Markov decision process. In a simplified setting, the optimal premium rule can be derived with dynamic programming methods. However, these classical methods are not feasible in a more realistic setting due to the dimens...

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Veröffentlicht in:ASTIN Bulletin : The Journal of the IAA Jg. 53; H. 2; S. 233 - 257
Hauptverfasser: Palmborg, Lina, Lindskog, Filip
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, USA Cambridge University Press 01.05.2023
Schlagworte:
Actuarial science
Approximation
Control algorithms
Deep learning
Dividends
Dynamical systems
Hedging
Insurance premiums
Markov analysis
Markov decision process
matematisk statistik
Mathematical Statistics
Mutual insurance companies
Premium control
Present value
Probability
reinforcement learning
Stochastic control theory
Stochastic models
Teaching methods
Markov decision process
Premium control
C60
G22
reinforcement learning
ISSN:0515-0361, 1783-1350, 1783-1350
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Abstract We consider a premium control problem in discrete time, formulated in terms of a Markov decision process. In a simplified setting, the optimal premium rule can be derived with dynamic programming methods. However, these classical methods are not feasible in a more realistic setting due to the dimension of the state space and lack of explicit expressions for transition probabilities. We explore reinforcement learning techniques, using function approximation, to solve the premium control problem for realistic stochastic models. We illustrate the appropriateness of the approximate optimal premium rule compared with the true optimal premium rule in a simplified setting and further demonstrate that the approximate optimal premium rule outperforms benchmark rules in more realistic settings where classical approaches fail.
AbstractList We consider a premium control problem in discrete time, formulated in terms of a Markov decision process. In a simplified setting, the optimal premium rule can be derived with dynamic programming methods. However, these classical methods are not feasible in a more realistic setting due to the dimension of the state space and lack of explicit expressions for transition probabilities. We explore reinforcement learning techniques, using function approximation, to solve the premium control problem for realistic stochastic models. We illustrate the appropriateness of the approximate optimal premium rule compared with the true optimal premium rule in a simplified setting and further demonstrate that the approximate optimal premium rule outperforms benchmark rules in more realistic settings where classical approaches fail.
Author Lindskog, Filip
Palmborg, Lina
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  givenname: Lina
  orcidid: 0000-0001-6338-3692
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  fullname: Palmborg, Lina
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  organization: Department of Mathematics Stockholm University Stockholm 106 91, Sweden
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  givenname: Filip
  surname: Lindskog
  fullname: Lindskog, Filip
  organization: Department of Mathematics Stockholm University Stockholm 106 91, Sweden
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Cites_doi 10.1007/BF00992701
10.1609/aaai.v25i1.7903
10.1080/03461238.1994.10413927
10.1109/ICASSP.2012.6288330
10.1023/A:1007678930559
10.1080/03461238.1983.10408686
10.2143/AST.23.2.2005092
10.1080/14697688.2019.1571683
10.1007/BF00992696
10.1145/1390156.1390240
10.1109/9.580874
10.1016/j.engappai.2019.01.010
10.1287/mnsc.2013.1788
ContentType Journal Article
Copyright The Author(s), 2023. Published by Cambridge University Press on behalf of The International Actuarial Association
The Author(s), 2023. Published by Cambridge University Press on behalf of The International Actuarial Association. This work is licensed under the Creative Commons Attribution License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: The Author(s), 2023. Published by Cambridge University Press on behalf of The International Actuarial Association
– notice: The Author(s), 2023. Published by Cambridge University Press on behalf of The International Actuarial Association. This work is licensed under the Creative Commons Attribution License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Issue 2
Keywords Markov decision process
Premium control
C60
G22
reinforcement learning
Language English
License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
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SubjectTerms Actuarial science
Approximation
Control algorithms
Deep learning
Dividends
Dynamical systems
Hedging
Insurance premiums
Markov analysis
Markov decision process
matematisk statistik
Mathematical Statistics
Mutual insurance companies
Premium control
Present value
Probability
reinforcement learning
Stochastic control theory
Stochastic models
Teaching methods
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