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Optimal commissions and subscriptions in mutual aid platforms

Published online by Cambridge University Press:  04 July 2023

Yixing Zhao  and
Yan Zeng
Yixing Zhao
Affiliation:
School of Finance, Guangdong University of Foreign Studies, Guangzhou 510006, PR China
Yan Zeng*
Affiliation:
Lingnan College, Sun Yat-sen University, Guangzhou 510275, PR China
*
Corresponding author: Yan Zeng; Email: zengy36@mail.sysu.edu.cn
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Abstract

This paper investigates an operation mechanism for mutual aid platforms to develop more sustainably and profitably. A mutual aid platform is an online risk-sharing platform for risk-heterogeneous participants, and the platform extracts revenues by charging participants commission and subscription fees. A modeling framework is proposed to identify the optimal commissions and subscriptions for mutual aid platforms. Participants are divided into different types based on their loss probabilities and values derived from the platform. We present how these commissions and subscriptions should be set in a mutual aid plan to maximize the platform’s revenues. Our analysis emphasized the importance of accounting for risk heterogeneity in mutual aid platforms. Specifically, different types of participants should be charged different commissions/subscriptions depending on their loss probabilities and values on the platform. Participants’ shared costs should be determined based on their loss probabilities. Adverse selection occurs on the platform if participants with different risks pay the same shared costs. Our results also show that the platform’s maximum revenue will be lower if the platform charges the same fee to all participants. The numerical results of a practical example illustrate that the optimal commission/subscription scheme and risk-sharing rule result in considerable improvements in platform revenue over the current scheme implemented by the platform.

Keywords

risk managementmutual aid platformoptimizationrevenue managementadverse selection
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 53 , Issue 3 , September 2023 , pp. 658 - 683
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of The International Actuarial Association

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References

Abdikerimova, S. and Feng, R. (2022) Peer-to-peer multi-risk insurance and mutual aid. European Journal of Operational Research, 299(2), 735–749.CrossRefGoogle Scholar
Belleflamme, P., Lambert, T. and Schwienbacher, A. (2014) Crowdfunding: Tapping the right crowd. Journal of Business Venturing, 29(5), 585–609.CrossRefGoogle Scholar
Bellos, I., Ferguson, M. and Toktay, L.B. (2017) The car sharing economy: Interaction of business model choice and product line design. Manufacturing and Service Operations Management, 19(2), 185–201.CrossRefGoogle Scholar
Benjaafar, S., Kong, G., Li, X. and Courcoubetis, C. (2019) Peer-to-peer product sharing: Implications for ownership, usage, and social welfare in the sharing economy. Management Science, 65(2), 477–493.CrossRefGoogle Scholar
Bimpikis, K., Candogan, O. and Saban, D. (2017) Spatial pricing in ride-sharing networks. Operations Research, 67(3), 744–769.CrossRefGoogle Scholar
Birge, J., Candogan, O., Chen, H. and Saban, D. (2021) Optimal commissions and subscriptions in networked markets. Manufacturing and Service Operations Management, 23(3), 547–730.Google Scholar
Boyle, E., Pesic, S., Jevtic, P. and Boscovic, D. (2021) Peer-to-peer insurance: Blockchain implications. Technical report, Society of Actuaries.Google Scholar
Breer, V.V. and Novikov, D.A. (2015) Threshold models of mutual insurance. Automation and Remote Control, 76(5), 897–908.CrossRefGoogle Scholar
Carlin, B.I. (2009) Strategic price complexity in retail financial markets. Journal of Financial Economics, 91(3), 278–87.CrossRefGoogle Scholar
China Insurance Regulatory Commission (2006) China Life Insurance Experienced Critical Illness Table (2006–2010). Data retrieved from http://bxjg.circ.gov.cn/web/site0/tab5225/info3891919.htm.Google Scholar
China Internet Network Information Center (2019) The 45th China Statistical Report on Internet Development. Data retrieved from http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/202004/t20200428_70974.htm.Google Scholar
Denuit, M. (2019) Size-biased transform and conditional mean risk sharing, with application to P2P insurance and tontines. Astin Bulletin, 49(3), 591–617.CrossRefGoogle Scholar
Denuit, M. (2020) Investing in your own and peers’ risks: The simple analytics of P2P insurance. Technical report.CrossRefGoogle Scholar
Denuit, M. and Robert, C.Y. (2020) From risk sharing to risk transfer: The analytics of collaborative insurance. Working Paper.Google Scholar
Denuit, M. and Robert, C.Y. (2021a) From risk sharing to pure premium for a large number of heterogeneous losses. Insurance: Mathematics and Economics, 96, 116–126.Google Scholar
Denuit, M. and Robert, C.Y. (2021b) Risk sharing under the dominant peer-to-peer property and casualty insurance business models. Risk Management and Insurance Review, 24, 181–205.CrossRefGoogle Scholar
Denuit, M. and Robert, C.Y. (2021c) Stop-loss protection for a large P2P insurance pool. Technical report.CrossRefGoogle Scholar
Donnelly, C. (2015) Actuarial fairness and solidarity in pooled annuity funds. ASTIN Bulletin, 45, 49–74. CrossRefGoogle Scholar
Feng, R., Liu, C. and Taylor, S. (2020) Peer-to-peer risk sharing with an application to flood risk pooling. Working paper.CrossRefGoogle Scholar
Handel, B., Hendel, I. and Whinston, M.D. (2015) Equilibria in health exchanges: Adverse selection versus reclassification risk. Econometrica, 83(4), 1261–1313.CrossRefGoogle Scholar
Jiang, B. and Lin, T. (2018) Collaborative consumption: Strategic and economic implications of product sharing. Management Science, 64(3), 1171–1188.CrossRefGoogle Scholar
Li, J., Fang, Z., Nie, C. and Chen, S. (2022) Optimal design for network mutual aid. Probability in the Engineering and Informational Sciences, 37(2), 567–597.CrossRefGoogle Scholar
Li, X., Huang, W., Chen, Y. and Zhou, W. (2019) The effects of an online inquiry service on gatekeeping systems with heterogeneous patients. Journal of Management Science and Engineering, 4(4), 211–227.CrossRefGoogle Scholar
Liu, S., Cheng, T. and Wang, H. (2020) Effects of attention and reliability on the performance of online medical crowdfunding projects: The moderating role of target amount. Journal of Management Science and Engineering, 5(3), 162–171.CrossRefGoogle Scholar
Lucas, R.E. (1980) Equilibrium in a pure currency economy. Economic Inquiry, 18(2), 203.CrossRefGoogle Scholar
Prescott, E.C. and Townsend, R.M. (1984a) General competitive analysis in an economy with private information. International Economic Review, 25(1), 1–20.CrossRefGoogle Scholar
Prescott, E.C. and Townsend, R.M. (1984b) Pareto optima and competitive equilibria with adverse selection and moral hazard. Econometrica: Journal of the Econometric Society, 52(1), 21–45.CrossRefGoogle Scholar
Research Institute of Ant Group (2020) Online Mutual Aid Industry White Paper.Google Scholar
Taylor, T.A. (2018) On-demand service platforms. Manufacturing and Service Operations Management, 20(4), 704–720.CrossRefGoogle Scholar
Wang, G., Qi, H., Xu, H. and Ryu, S. (2020) A mixed behaviour equilibrium model with mode choice and its application to the endogenous demand of automated vehicles. Journal of Management Science and Engineering, 5(4), 227–248.CrossRefGoogle Scholar
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