Document Type : Research Article

Authors

1 Department of Actuarial Science, Shahid Beheshti University, Theran, Iran

2 Department of Actuarial Science, Shahid Beheshti University, Tehran, Iran

Abstract

‎An important question in non life insurance research is the ‎estimation of number of future payments and corresponding ‎amount of them. A ‎loss reserve is the money set aside by insurance companies to pay ‎policyholders claims on their policies. The policyholder behavior for reporting claims after its ‎occurrence have significant effect on the costs of the insurance ‎company. This article considers the problem of predicting the amount and number ‎of claims that have been incurred but not reported, ‎say IBNR‎. ‎Using the delay probabilities in monthly level, ‎calculated by the Zero Inflated Gamma Mixture distribution, ‎it predicts IBNR's‎ ‎loss reserve. ‎‎The model advantage in the IBNR reserve is insurers can predict ‎the number of future claims for each future date. ‎This enables ‎them to change the claim reporting process. The practical applications of our findings are applied against a third party liability (TPL) insurance loss portfolio. Additional information about claim can be considered in the loss reserving ‎model and making the prediction of amount more accurate.

Keywords

[1] K. ANTONIO, R. PLAT, Micro-level stochastic loss reserving for general insurance, Scandinavian Actuarial Journal, 7(2014), 649-669.
[2] A.L. BADESCU, X.S. LIN, D. TANG, A marked Cox model for the number of IBNR claims: theory, Insurance: Mathematics and Economics, 69(2016), 29-37.
[3] A.L. BADESCU, T. CHEN, X.S. LIN, AND D. TANG, A marked COX model for the number of IBNR claims: estimation and application, ASTIN Bulletin: The Journal of the IAA, 49(2019), 709-739.
[4] J. CREVECOEUR, K. ANTONIO, AND R. VERBELEN, A Time Change Strategy to Model Reporting Delay Dynamics in Claims Reserving, The 3rd Europen Actuarial Journal conference, (2018), KU Leuven, Belgium.
[5] J. CREVECOEUR, K. ANTONIO, AND R. VERBELEN, Modeling the number of hidden events subject to observation delay, European Journal of Operational Research, 277(2019), 930-944.
[6] V.R. DA SILVA, A. YONGACOGLU, EM algorithm on the approximation of arbitrary pdfs by gaussian, gamma and lognormal mixture distributions, The 7th IEEE Latin-American Conference on Communications, LATINCOM, IEEE,
(2015).
[7] M. GHARIB, Two characterisations of a gamma mixture distribution, Bulletin of the Australian Mathematical Society, 52(1995), 353-358.
[8] M.D. MARTINEZ-MIRANDA, J.P. NIELSEN, AND R.J. VERRALL, Double chain ladder, ASTIN Bulletin: The Journal of the IAA, 42(2012), 59-76.
[9] M.D. MARTINEZ-MIRANDA, J.P. NIELSEN AND R.J. VERRALL, Double chain ladder and Bornhuetter-Ferguson, North American Actuarial Journal, 17(2013a), 101-113.
[10] M.D. MARTINEZ-MIRANDA, J.P. NIELSEN, S. SPERLICH, AND R.J. VERRALL, Continuous Chain Ladder: Reformulating and generalizing a classical insurance problem, Expert Systems with Applications, 40(2013b), 5588-5603.
[11] T.K. MOON, The expectation-maximization algorithm, IEEE Signal Processing Magazine, 13(1996), 48-60.
[12] R. VERBELEN, K. ANTONIO, G. CLAESKENS, AND J. CREVECOEUR, Modeling the occurrence of events subject to a reporting delay via an EM algorithm, arXiv:1909.08336, (2019).
[13] R.J. VERRALL, J.P. NIELSEN, AND A. H. JESSEN, Prediction of RBNS and IBNR Claims using Claim Amounts and Claim Counts, ASTIN Bulletin: The Journal of the IAA, 40(2010), 871-887.
[14] R.J. VERRALL, M.V. WUTHRICH, Understanding reporting delay in general insurance, Risks, 4(2016),1-36.