Publication:
Reinforcement learning for pricing strategy optimization in the insurance industry

Simple item page
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Planificación y Aprendizajees
dc.contributor.authorKrasheninnikova, Elena
dc.contributor.authorGarcía Polo, Francisco Javier
dc.contributor.authorLosada Maestre, Roberto
dc.contributor.authorFernández Rebollo, Fernando
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2019-12-18T15:35:00Z
dc.date.available2021-04-01T23:00:06Z
dc.date.issued2019-04-01
dc.description.abstractPricing is a fundamental problem in the banking sector, and is closely related to a number of financial products such as credit scoring or insurance. In the insurance industry an important question arises, namely: how can insurance renewal prices be adjusted? Such an adjustment has two conflicting objectives. On the one hand, insurers are forced to retain existing customers, while on the other hand insurers are also forced to increase revenue. Intuitively, one might assume that revenue increases by offering high renewal prices, however this might also cause many customers to terminate their contracts. Contrarily, low renewal prices help retain most existing customers, but could negatively affect revenue. Therefore, adjusting renewal prices is a non-trivial problem for the insurance industry. In this paper, we propose a novel modelization of the renewal price adjustment problem as a sequential decision problem and, consequently, as a Markov decision process (MDP). In particular, this study analyzes two different strategies to carry out this adjustment. The first is about maximizing revenue analyzing the effect of this maximization on customer retention, while the second is about maximizing revenue subject to the client retention level not falling below a given threshold. The former case is related to MDPs with a single criterion to be optimized. The latter case is related to Constrained MDPs (CMDPs) with two criteria, where the first one is related to optimization, while the second is subject to a constraint. This paper also contributes with the resolution of these models by means of a model-free Reinforcement Learning algorithm. Results have been reported using real data from the insurance division of BBVA, one of the largest Spanish companies in the banking sector.es
dc.description.sponsorshipThis work has been partially funded by the TIN2015-65686-C5 Spanish Ministerio de Economía y Competitividad project and FEDER funds. Javier García is partially supported by the Comunidad de Madrid (Spain) funds under the project 2016-T2/TIC-1712.en
dc.identifier.bibliographicCitationKrasheninnikova, E., García, J., Maestre, R., & Fernández, F. (2019). Reinforcement learning for pricing strategy optimization in the insurance industry. Engineering Applications of Artificial Intelligence, 80, April 2019, pp. 8-19en
dc.identifier.doihttps://doi.org/10.1016/j.engappai.2019.01.010
dc.identifier.issn0952-1976
dc.identifier.publicationfirstpage8
dc.identifier.publicationlastpage19
dc.identifier.publicationtitleEngineering applications of artificial intelligenceen
dc.identifier.publicationvolume80
dc.identifier.urihttps://hdl.handle.net/10016/29389
dc.identifier.uxxiAR/0000023548
dc.language.isoenges
dc.publisherElsevieres
dc.relation.projectIDGobierno de España. TIN2015-65686-C5-1-Res
dc.relation.projectIDComunidad de Madrid. 2016-T2/TIC-1712.es
dc.rights© 2019 Elsevier Ltd. All rights reserved.es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherPricing strategy optimizationen
dc.subject.otherReinforcement learningen
dc.titleReinforcement learning for pricing strategy optimization in the insurance industryen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
reinforcement_EAAI_2019_ps.pdf
Size:
776.78 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
DI - PLG - Artículos de Revistas