In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.
References
[1]
Richman, R. (2018) AI in Actuarial Science. SSRN Electronic Journal.
[2]
Balona, C. and Richman, R. (2020) The Actuary and IBNR Techniques: A Machine Learning Approach. SSRN Electronic Journal.
[3]
Panlilio, A., Canagaretna, B., Perkins, S., du Preez, V. and Lim, Z. (2018) Practical Application of Machine Learning within Actuarial Work. Institute and Faculty of Actuaries, London.
Pentikäinen, T. and Rantala, J. (1992) A Simulation Procedure for Comparing Different Claims Reserving Methods. ASTIN Bulletin, 22, 191-216. https://doi.org/10.2143/ast.22.2.2005115
[6]
Radtke, M., Schmidt, K.D. and Schnaus, A. (2016) Handbook on Loss Reserving. Springer. https://doi.org/10.1007/978-3-319-30056-6
[7]
Bjarnason, T. and Sjögren, M. (2014) Insurance Loss Reserving. LUP Student Papers, Lund University.
[8]
Murphy, D. (1994) The Bornhuetter-Ferguson Principle. North American Actuarial Journal, 12, 377-379.
[9]
Mack, T. (1993) Distribution-Free Calculation of the Standard Error of Chain Ladder Reserve Estimates. ASTIN Bulletin, 23, 213-225. https://doi.org/10.2143/ast.23.2.2005092
[10]
England, P.D. (2018) Actuarial Reserving: An Overview of Chain Ladder Methodologies. CAS Monograph Series, 5, 1-25.
[11]
Bartley, J.W. and Boardman, C.M. (1990) The Relevance of Inflation Adjusted Accounting Data to the Prediction of Corporate Takeovers. Journal of Business Finance & Accounting, 17, 53-72. https://doi.org/10.1111/j.1468-5957.1990.tb00549.x
[12]
Schwert, G.W. (1981) The Adjustment of Stock Prices to Information about Inflation. The Journal of Finance, 36, 15-29. https://doi.org/10.1111/j.1540-6261.1981.tb03531.x
[13]
Hunsicker, C.F. (2023) On Machine Learning Approaches to Forecast Non-Life Insurers Loss Reserves. Master’s Thesis, University of Twente.
[14]
Blier-Wong, C., Cossette, H., Lamontagne, L. and Marceau, E. (2020) Machine Learning in Property and Casualty Insurance: A Review for Pricing and Reserving. SSRN Electronic Journal.
[15]
Xiong, L., Manathunga, V., Luo, J., Dennison, N., Zhang, R. and Xiang, Z. (2023) Autoreserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods. Risks, 11, Article 131. https://doi.org/10.3390/risks11070131
[16]
Tuininga, F. (2022) A Machine Learning Approach for Modeling Frequency and Severity. Master’s Thesis, University of Twente.
[17]
Wilson, A.A., Nehme, A., Dhyani, A. and Mahbub, K. (2024) A Comparison of Generalised Linear Modelling with Machine Learning Approaches for Predicting Loss Cost in Motor Insurance. Risks, 12, Article 62. https://doi.org/10.3390/risks12040062
[18]
Panjee, P. and Amornsawadwatana, S. (2024) A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context. Risks, 12, Article 25. https://doi.org/10.3390/risks12020025
[19]
Taylor, G. (2000) Loss Reserving: An Actuarial Perspective. Kluwer Academic Publishers.
[20]
England, P.D. and Verrall, R.J. (2002) Stochastic Claims Reserving in General Insurance. British Actuarial Journal, 8, 443-518. https://doi.org/10.1017/s1357321700003809
[21]
Wüthrich, M.V. and Merz, M. (2008) Stochastic Claims Reserving Methods in Insurance. Wiley.
[22]
Meyers, G. (2007) The Common Pitfalls of Loss Reserving. Casualty Actuarial Society, Arlington, 1-32.
[23]
Kuo, W., Tsai, C.-F. and Chen, Y.-K. (2018) A Comparison of Machine Learning Methods for Insurance Claims Prediction. Soft Computing, 22, 2769-2783.
[24]
Richman, R. and Wüthrich, M.V. (2020) A Neural Network Extension of the Lee-Carter Model to Multiple Populations. Annals of Actuarial Science, 14, 72-91.
[25]
Gabrielli, A. and Wüthrich, M.V. (2018) Bayesian Neural Networks for Insurance Claims Reserving. Risks, 6, Article 18.
[26]
Antonio, K. and Plat, R. (2013) Micro-Level Stochastic Loss Reserving for General Insurance. Scandinavian Actuarial Journal, 2014, 649-669. https://doi.org/10.1080/03461238.2012.755938
[27]
Clark, D.R. and Rangelova, D. (2015) Accident Year/Development Year Interactions. Casualty Actuarial Society. Arlington.
[28]
Parodi, P. (2013) Triangle-Free Reserving. British Actuarial Journal, 19, 168-218. https://doi.org/10.1017/s1357321713000093
[29]
Catarina Ferreira de Jesus de, S. (2023) IBNR Techniques in Health Insurance: A Machine Learning Approach. Ph.D. Dissertation, Universidade NOVA de Lisboa.
[30]
Cipra, T. and Hendrych, R. (2023) Holt-Winters Method for Run-off Triangles in Claims Reserving. European Actuarial Journal, 13, 815-836. https://doi.org/10.1007/s13385-023-00348-2
[31]
Matthews, S. and Hartman, B. (2022) Machine Learning in Ratemaking, an Application in Commercial Auto Insurance. Risks, 10, Article 80. https://doi.org/10.3390/risks10040080
[32]
Kothari, C.R. (2004) Research Methodology: Methods and Techniques. New Age International.
[33]
Gesmann, M., Murphy, D., Zhang, Y.W., Carrato, A., Crupi, G., Dutang, C., et al. (2023) Statistical Methods and Models for Claims Reserving in General Insurance [R Package ChainLadder Version 0.2.11].
[34]
Carrato, A., Concina, F., Gesmann, M., Murphy, D., Wuthrich, M. and Zhang, W. (2015) Claims Reserving with R: ChainLadder-0.2.2 Package Vignette.
[35]
de Oliveira, M.C.F. and Levkowitz, H. (2003) From Visual Data Exploration to Visual Data Mining: A Survey. IEEE Transactions on Visualization and Computer Graphics, 9, 378-394. https://doi.org/10.1109/tvcg.2003.1207445
[36]
Kuo, K. (2019) Deeptriangle: A Deep Learning Approach to Loss Reserving. Risks, 7, Article 97. https://doi.org/10.3390/risks7030097
[37]
Zhang, Y., Wen, J., Yang, G., He, Z. and Wang, J. (2019) Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion. Applied Sciences, 9, Article 1908. https://doi.org/10.3390/app9091908
[38]
Taylor, G. (2019) Loss Reserving Models: Granular and Machine Learning Forms. Risks, 7, Article 82. https://doi.org/10.3390/risks7030082
[39]
Yousuf, W., Stansfield, J., Malde, K., Mirin, N., Walton, R., Thorpe, B., et al. (2021) The IFRS 17 Contractual Service Margin: A Life Insurance Perspective. British Actuarial Journal, 26, e2. https://doi.org/10.1017/s1357321721000015
[40]
Araiza Iturria, C.A., Godin, F. and Mailhot, M. (2021) Tweedie Double GLM Loss Triangles with Dependence within and across Business Lines. European Actuarial Journal, 11, 619-653. https://doi.org/10.1007/s13385-021-00267-0
[41]
Shiu, Y.M., Wang, C.F., Adams, A. and Shin, Y.C. (2012) On the Determinants of Derivative Hedging by Insurance Companies: Evidence from Taiwan. Asian Economic and Financial Review, 2, 538.
[42]
Crevecoeur, J., Antonio, K., Desmedt, S. and Masquelein, A. (2023) Bridging the Gap between Pricing and Reserving with an Occurrence and Development Model for Non-Life Insurance Claims. ASTIN Bulletin, 53, 185-212. https://doi.org/10.1017/asb.2023.14
