This study investigates several pooling risk sharing schemes and the measurement of their pool quality as peer to peer (P2P) insurance platforms. The P2P insurance is an emerging and growing insurance scheme that helps each other compensate the pool’s total losses by funding and risk sharing ex-post with a certain rule among the pool participants. The new scheme comes from preliminary empirical research regarding existing P2P insurance. In the end, the schemes are characterized by the rule and the adjacency matrix of the pool, which leverages network theory, and the new risk sharing rule looks like adjusted version of the typical network adjacency matrix, which identifies indicators of pool quality of a new scheme. While assessing effectiveness among several risk sharing rules, it finds that, especially for the new risk sharing scheme, those matrix entropy serves as an indicator of pool quality like volatility and network centrality. Examples illustrate the effectiveness of the proposed model in assessing pool quality. This study contributes to the literature by proposing a new risk sharing scheme, which is based on preliminary empirical research, considering pool quality, and introducing the adjusted version of adjacency matrix as a rule and a tool to model relative relationships among pool participants. It expands the understanding of risk sharing mechanisms in P2P insurance platforms and provides valuable insights for pool management and quality assessment. The study highlights the importance of considering relative relationships among pool participants.
References
[1]
Feng, R., Liu, C. and Taylor, S. (2022) Peer-to-Peer Risk Sharing with an Application to Flood Risk Pooling. AnnalsofOperationsResearch, 321, 813-842. https://doi.org/10.1007/s10479-022-04841-x
[2]
Besley, T., Coate, S., Loury, G. (1993) The Economics of Rotating Savings and Credit Associations. TheAmericanEconomicReview, 83, 792-810. http://www.jstor.org/stable/2117579
[3]
Besley, T., Coate, S. and Loury, G. (1994) Rotating Savings and Credit Associations, Credit Markets and Efficiency. TheReviewofEconomicStudies, 61, 701-719. https://doi.org/10.2307/2297915
[4]
Denuit, M. (2020) Investing in Your Own and Peers’ Risks: The Simple Analytics of P2P Insurance. EuropeanActuarialJournal, 10, 335-359. https://doi.org/10.1007/s13385-020-00238-x
[5]
Chen, Z., Feng, R., Hu, W. and Mao, Y. (2023) Optimal Risk Pooling of Peer-to-Peer Insurance. SSRNElectronicJournal. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4498641
[6]
Charpentier, A., Kouakou, L., Lowe, M., Ratz, P. and Vermet, F. (2021) Collaborative Insurance Sustainability and Network Structure. Papers 2107.02764. https://arxiv.org/pdf/2107.02764
[7]
Nakamaru, Y. (2020) Understanding Social System by Trust. Kyoritsu Syuppan, Japan. https://www.kyoritsu-pub.co.jp/book/b10003277.html
Schumacher, J.M. (2018) Linear versus Nonlinear Allocation Rules in Risk Sharing under Financial Fairness. ASTINBulletin, 48, 995-1024. https://doi.org/10.1017/asb.2018.25
[11]
Sun, Z. and Abbas, A.E. (2018) Pareto Optimality and Risk Sharing in Group Utility Functions. IISETransactions, 50, 298-306. https://doi.org/10.1080/24725854.2017.1394601
Abbas, A.E. and Sun, Z. (2015) Multiattribute Utility Functions Satisfying Mutual Preferential Independence. OperationsResearch, 63, 378-393. https://doi.org/10.1287/opre.2015.1350
[14]
Feng, R., Liu, M. and Zhang, N. (2022) A Unified Theory of Decentralized Insurance. SSRNElectronicJournal. https://doi.org/10.2139/ssrn.4013729
[15]
Denuit, M., Dhaene, J. and Robert, C.Y. (2022) Risk‐Sharing Rules and Their Properties, with Applications to Peer‐to‐Peer Insurance. JournalofRiskandInsurance, 89, 615-667. https://doi.org/10.1111/jori.12385
[16]
Denuit, M. and Dhaene, J. (2012) Convex Order and Comonotonic Conditional Mean Risk Sharing. Insurance:MathematicsandEconomics, 51, 265-270. https://doi.org/10.1016/j.insmatheco.2012.04.005
[17]
Denuit, M. and Robert, C.Y. (2021) From Risk Sharing to Pure Premium for a Large Number of Heterogeneous Losses. Insurance:MathematicsandEconomics, 96, 116-126. https://doi.org/10.1016/j.insmatheco.2020.11.006
[18]
Denuit, M. and Robert, C.Y. (2021) Risk Sharing under the Dominant Peer‐to‐Peer Property and Casualty Insurance Business Models. RiskManagementandInsuranceReview, 24, 181-205. https://doi.org/10.1111/rmir.12180
[19]
Denuit, M. and Robert, C.Y. (2021) Conditional Mean Risk Sharing in the Individual Model with Graphical Dependencies. AnnalsofActuarialScience, 16, 183-209. https://doi.org/10.1017/s1748499521000166
[20]
Arrow, K. (1963) Uncertainty and the Welfare Economics of Medical Care. TheAmeri-canEconomicReview, 53, 941-973.
[21]
Bühlmann, H. and Jewell, W.S. (1979) Optimal Risk Exchanges. ASTINBulletin, 10, 243-262. https://doi.org/10.1017/s0515036100005882
[22]
Skogh, G. (1999) Risk Sharing Institutions for Unpredictable Losses. JournalofInstitutionalandTheoreticalEconomics, 155, 505-515.
[23]
Abdikerimova, S. and Feng, R. (2022) Peer-to-Peer Multi-Risk Insurance and Mutual Aid. EuropeanJournalofOperationalResearch, 299, 735-749. https://doi.org/10.1016/j.ejor.2021.09.017
[24]
Bienenstock, E.J. and Bonacich, P. (2021) Eigenvector Centralization as a Measure of Structural Bias in Information Aggregation. TheJournalofMathematicalSociology, 46, 227-245. https://doi.org/10.1080/0022250x.2021.1878357
[25]
Chen, X., Chong, Z., Giudici, P. and Huang, B. (2022) Network Centrality Effects in Peer to Peer Lending. PhysicaA:StatisticalMechanicsand Its Applications, 600, Article 127546. https://doi.org/10.1016/j.physa.2022.127546
[26]
Afuah, A. (2012) Are Network Effects Really All about Size? The Role of Structure and Conduct. StrategicManagementJournal, 34, 257-273. https://doi.org/10.1002/smj.2013
[27]
Iacobucci, D., McBride, R. and Popovich, D.L. (2017) Eigenvector Centrality: Illustrations Supporting the Utility of Extracting More than One Eigenvector to Obtain Additional Insights into Networks and Interdependent Structures. JournalofSocialStructure, 18, 1-23. https://doi.org/10.21307/joss-2018-003
[28]
Newman, M. (2018) Networks. Oxford University Press.
[29]
Yamashita, M. (2023) Cybersecurity Risk Sharing with Network Meritocracy, Promotion of Cybersecurity for Small & Medium Enterprises. Toyo University Keiei Ronsyu, 65-79.
[30]
Omar, Y.M. and Plapper, P. (2020) A Survey of Information Entropy Metrics for Complex Networks. Entropy, 22, Article 1417. https://doi.org/10.3390/e22121417
[31]
Esscher, F. (1932) On the Probability Function in the Collective Theory of Risk. Scandinavian Actuarial Journal, 15, 175-195. https://doi.org/10.1080/03461238.1932.10405883
[32]
Gerber, H.U. and Shiu, E.S.W. (1994) Option Pricing by Esscher Transforms. TransactionsoftheSocietyofActuaries, 46, 99-191.
[33]
Martínez Pería, F.D., Massey, P.G. and Silvestre, L.E. (2005) Weak Matrix Majorization. LinearAlgebraand Its Applications, 403, 343-368. https://doi.org/10.1016/j.laa.2005.02.003
[34]
Scharfenaker, E. and Yang, J. (2020) Maximum Entropy Economics. TheEuropeanPhysicalJournalSpecialTopics, 229, 1577-1590. https://doi.org/10.1140/epjst/e2020-000029-4