IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v6y2018i1p9-d130922.html
   My bibliography  Save this article

Price and Profit Optimization for Financial Services

Author

Listed:
  • Catalina Bolancé

    (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, Av. Diagonal, 690, 08034 Barcelona, Spain)

  • Montserrat Guillen

    (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, Av. Diagonal, 690, 08034 Barcelona, Spain)

  • Jens Perch Nielsen

    (Cass Business School, City, University of London, 106 Bunhill Row, London EC1Y 8TZ, UK)

  • Fredrik Thuring

    (Cass Business School, City, University of London, 106 Bunhill Row, London EC1Y 8TZ, UK)

Abstract

Prospective customers of financial and insurance products can be targeted based on the profit the provider expects to earn from them. We present a model for individual expected profit and two alternatives for calculating optimal personalized prices that maximize the expected profit. For one of these alternatives, we obtain a closed-form expression for the price offered to each prospective customer; for the other, we need to use a numerical approximation. In both approaches, the profits generated by prospective customers are not immediately observed, given that the products sold by these companies have a risk component. We assume that willingness to pay is heterogeneous and apply our methodology using real data from a European insurance company. Our study indicates that a substantial boost in profits can be expected when applying the simplest optimal pricing method proposed.

Suggested Citation

  • Catalina Bolancé & Montserrat Guillen & Jens Perch Nielsen & Fredrik Thuring, 2018. "Price and Profit Optimization for Financial Services," Risks, MDPI, vol. 6(1), pages 1-12, February.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:1:p:9-:d:130922
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/6/1/9/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/6/1/9/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Montserrat Guillén & Ana María Pérez-Marín & Montserrat Guillén, 2011. "A logistic regression approach to estimating customer profit loss due to lapses in insurance," Working Papers XREAP2011-13, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2011.
    2. von Lanzenauer, Christoph Haehling & Wright, Don D., 1991. "Operational research and insurance," European Journal of Operational Research, Elsevier, vol. 55(1), pages 1-13, November.
    3. Frees, Edward W. & Valdez, Emiliano A., 2008. "Hierarchical Insurance Claims Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1457-1469.
    4. G. Dionne & N. Doherty & N. Fombaron, 2000. "Adverse Selection in Insurance Markets," THEMA Working Papers 2000-21, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    5. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    6. Jean-Philippe Boucher & Michel Denuit & Montserrat Guillén, 2007. "Risk Classification for Claim Counts," North American Actuarial Journal, Taylor & Francis Journals, vol. 11(4), pages 110-131.
    7. Daniela Laas & Hato Schmeiser & Joël Wagner, 2016. "Empirical Findings on Motor Insurance Pricing in Germany, Austria and Switzerland," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 41(3), pages 398-431, July.
    8. Jean‐Philippe Boucher & Michel Denuit & Montserrat Guillen, 2009. "Number of Accidents or Number of Claims? An Approach with Zero‐Inflated Poisson Models for Panel Data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(4), pages 821-846, December.
    9. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
    10. Michael Rothschild & Joseph Stiglitz, 1976. "Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 90(4), pages 629-649.
    11. Haehling von Lanzenauer, Christoph & Wright, Don D., 1991. "Operational research and insurance," European Journal of Operational Research, Elsevier, vol. 52(2), pages 129-141, May.
    12. Wagner A. Kamakura & Bruce S. Kossar & Michel Wedel, 2004. "Identifying Innovators for the Cross-Selling of New Products," Management Science, INFORMS, vol. 50(8), pages 1120-1133, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Himchan Jeong & Guojun Gan & Emiliano A. Valdez, 2018. "Association Rules for Understanding Policyholder Lapses," Risks, MDPI, vol. 6(3), pages 1-18, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lingxiu Dong & Brian Tomlin, 2012. "Managing Disruption Risk: The Interplay Between Operations and Insurance," Management Science, INFORMS, vol. 58(10), pages 1898-1915, October.
    2. Shi, Peng & Valdez, Emiliano A., 2011. "A copula approach to test asymmetric information with applications to predictive modeling," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 226-239, September.
    3. Liu, Zhongyi & Li, Mengyu & Lei, Ying & Zhai, Xin, 2022. "A joint strategy based on ordering and insurance for mitigating the effects of supply chain disruption on risk-averse firms," International Journal of Production Economics, Elsevier, vol. 244(C).
    4. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    5. Payandeh Najafabadi Amir T. & MohammadPour Saeed, 2018. "A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-31, July.
    6. Robert J. Gary-Bobo & Alain Trannoy, 2015. "Optimal student loans and graduate tax under moral hazard and adverse selection," RAND Journal of Economics, RAND Corporation, vol. 46(3), pages 546-576, September.
    7. Daniel McFadden & Carlos Noton & Pau Olivella, "undated". "Remedies for Sick Insurance," Working Papers 620, Barcelona School of Economics.
    8. repec:mea:meawpa:12259 is not listed on IDEAS
    9. Lluís Bermúdez & Dimitris Karlis & Isabel Morillo, 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models," Risks, MDPI, vol. 8(1), pages 1-13, January.
    10. Christensen, Bent Jesper & Parra-Alvarez, Juan Carlos & Serrano, Rafael, 2021. "Optimal control of investment, premium and deductible for a non-life insurance company," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 384-405.
    11. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
    12. Michel-Kerjan Erwann & de Marcellis-Warin Nathalie, 2006. "Public-Private Programs for Covering Extreme Events: The Impact of Information Distribution on Risk-Sharing," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 1(2), pages 1-30, February.
    13. Martin Spindler & Joachim Winter & Steffen Hagmayer, 2014. "Asymmetric Information in the Market for Automobile Insurance: Evidence From Germany," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(4), pages 781-801, December.
    14. Dionne, Georges & Michaud, Pierre-Carl & Pinquet, Jean, 2013. "A review of recent theoretical and empirical analyses of asymmetric information in road safety and automobile insurance," Research in Transportation Economics, Elsevier, vol. 43(1), pages 85-97.
    15. Yves Staudt & Joël Wagner, 2021. "Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance," Risks, MDPI, vol. 9(3), pages 1-28, March.
    16. Mihaela Covrig & Iulian Mircea & Gheorghita Zbaganu & Alexandru Coser & Alexandru Tindeche, 2015. "Using R In Generalized Linear Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 33-45, September.
    17. Roozbeh Hosseini, 2015. "Adverse Selection in the Annuity Market and the Role for Social Security," Journal of Political Economy, University of Chicago Press, vol. 123(4), pages 941-984.
    18. Bermúdez, Lluís & Karlis, Dimitris, 2012. "A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3988-3999.
    19. André Bonfrer & Xavier Drèze, 2009. "Real-Time Evaluation of E-mail Campaign Performance," Marketing Science, INFORMS, vol. 28(2), pages 251-263, 03-04.
    20. Michael Hoy & Michael Ruse, 2005. "Regulating Genetic Information in Insurance Markets," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 8(2), pages 211-237, September.
    21. repec:mea:meawpa:12260 is not listed on IDEAS
    22. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:6:y:2018:i:1:p:9-:d:130922. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.