The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.
References
[1]
Mihaela, D. (2015) A Review of Theoretical Concepts and Empirical Literature of Non-Life Insurance Pricing. Procedia Economics and Finance, 20, 157-162. https://doi.org/10.1016/S2212-5671(15)00060-X
[2]
Anderson, J.D., Bolton, C.G., Callan, G.L., Cross, M., Howard, S.K., Mitchell, G.R.J., Murphy, K.P., Rakow, J.C., Stirling, P.A. and Welsh, G.E. (2007) General Insurance Premium Rating—The Way Forward. Summary of the Recommendations of the General Insurance Premium Rating Working Party (GRIP). British Actuarial Journal, 13, 637-644. https://doi.org/10.1017/S1357321700001586
[3]
Thomas, A.M. and Thomas, R.G. (1997) The Right to Underwrite? An Actuarial Perspective with a Difference. Journal of Actuarial Practice, 5, 125-146.
[4]
Mihaela, D. (2015) Auto Insurance Premium Calculation Using Generalized Linear Models. Procedia Economics and Finance, 20, 147-156. https://doi.org/10.1016/S2212-5671(15)00059-3
[5]
de Jong, C.H. (2021) Risk Classification and the Balance of Information in Insurance; an Alternative Interpretation of the Evidence. Risk Management and Insurance Review, 24, 445-461. https://doi.org/10.1111/rmir.12198
[6]
Nicholas, Y., Raymond, L., Jyeh, O.M. and Liew, J.Y. (2019) Literature Review: Artificial Intelligence and Its Use in Actuarial Work. Actuarial Innovation & Technology, Society of Actuaries.
[7]
Tuininga, F. (2022) A Machine Learning Approach for Modeling Frequency and Severity. Master’s Thesis, University of Twente.
[8]
Chaturvedi, K.K. and Singh, V.B. (2012) Determining Bug Severity Using Machine Learning Techniques. 2012 CSI Sixth International Conference on Software Engineering (CONSEG). Indore, 5-7 September 2012, 1-6. https://doi.org/10.1109/CONSEG.2012.6349519
[9]
Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
[10]
Taylor, G.C. (2000) Loss Reserving: Past, Present and Future. North American Actuarial Journal, 4, 1-17.
[11]
Meyers, G. (2015) Stochastic Loss Reserving Using Bayesian MCMC Models. Casualty Actuarial Society, Arlington.
[12]
Duncan, A., McPhail, P. and Siegel, A. (2021) Data Science for Actuaries. British Actuarial Journal, 26.
[13]
Clark, D.R. (2005) LDF Curve-Fitting and Stochastic Loss Reserving: A Maximum Likelihood Approach. Casualty Actuarial Society Forum, 41-91.
[14]
Goldburd, M., Khare, A. and Tevet, D. (2016) Generalized Linear Models for Insurance Rating. Casualty Actuarial Society, Arlington.
[15]
Wuethrich, M.V. and Merz, M. (2020) Stochastic Claims Reserving Methods in Insurance. Wiley.
[16]
Antonio, K. and Plat, R. (2014) Micro-Level Stochastic Loss Reserving for General Insurance. Scandinavian Actuarial Journal, 2014, 649-669. https://doi.org/10.1080/03461238.2012.755938
[17]
Haberman, S. and Renshaw, A.E. (1996) Generalized Linear Models and Actuarial Science. Journal of the Royal Statistical Society: Series D, 45, 407-436. https://doi.org/10.2307/2988543
[18]
Friedman, J.H. (2001) Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29, 1189-1232. https://doi.org/10.2307/2988543
[19]
Antonio, K., Bard, B. and Ohlsson, E. (2010) A Neural Network Approach to Loss Reserving. ASTIN Bulletin: The Journal of the IAA, 40, 449-477.
[20]
Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
[21]
Kuhn, M. and Johnson, K. (2013) Applied Predictive Modeling. Springer Science & Business Media. https://doi.org/10.1007/978-1-4614-6849-3
[22]
Chen, T. and Guestrin, C. (2016) XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 785-794. https://doi.org/10.1145/2939672.2939785
[23]
Styrud, L. (2017) Risk Premium Prediction of Car Damage Insurance Using Artificial Neural Networks and Generalized Linear Models.
[24]
Verbelen, R. (2017) Data Analytics for Insurance Loss Modelling, Telematics Pricing and Claims Reserving. PhD Thesis, Ku Leuven.
[25]
El Kassimi, F. and Zahi, J. (2021) Non-Life Insurance Ratemaking Techniques : A Literature Review of the Classic Methods. International Journal of Accounting, Finance, Auditing, Management and Economics, 2, 344-361.
[26]
Jamal, S., Canto, S., Fernwood, R., Giancaterino, C., Lo-Renzoinvernizzi, M., Korzhynska, T., Martin, Z. and Shen, H. (2018) Machine Learning & Traditional Methods Synergy in Non-Life Reserving. Report of the ASTIN Working Party of the International Actuarial Association. https://www.actuaries.org/IAA/Documents/ASTIN/ASTINMLTMS%20ReportSJAMAL.pdf
[27]
Burri, R.D., Burri, R., Bojja, R.R. and Buruga, S.R. (2019) Insurance Claim Analysis Using Machine Learning Algorithms. International Journal of Innovative Technology and Exploring Engineering, 8, 577-582. https://doi.org/10.35940/ijitee.F1118.0486S419
[28]
Filzmoser, P., Hron, K. and Templ, M. (2018) Correlation Analysis. In: Filzmoser, P., Hron, K. and Templ, M., Eds., Applied Compositional Data Analysis, Springer, 149-162. https://doi.org/10.1007/978-3-319-96422-5_8
[29]
Cohen, J., Cohen, P., West, S.G. and Aiken, L.S. (2013) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge. https://doi.org/10.4324/9780203774441
[30]
Comrey, A.L. and Lee, H.B. (2013) A First Course in Factor Analysis. Psychology Press. https://doi.org/10.4324/9781315827506
[31]
Jolliffe, I.T. (2002) Principal Component Analysis for Special Types of Data. In: Jolliffe, I.T., Ed., Principal Component Analysis, Springer, 338-372. https://doi.org/10.1007/0-387-22440-8_13
[32]
Williams, B., Onsman, A. and Brown, T. (2010) Exploratory Factor Analysis: A Five-Step Guide for Novices. Paramedicine, 8, 1-13. https://doi.org/10.33151/ajp.8.3.93
[33]
Oner, C. (2012) Inflation: Prices on the Rise. International Monetary Fund.
[34]
Mkrtychev, S. and Enik, O. (2019) Automated Underwriting Control in a Regional Insurance Company. Proceedings of the International Scientific Conference “Far East Con” (ISCFEC 2018), 291-293. https://doi.org/10.2991/iscfec-18.2019.66
[35]
Boudreault, M. and Renaud, J.-F. (2019) Actuarial Finance: Derivatives, Quantitative Models and Risk Management. John Wiley & Sons. https://doi.org/10.1002/9781119526438
[36]
Francis, G. (2019) Enterprise Risk Management (ERM): Key Risks, Responses and Applications. 2019 Enterprise Risk Management Symposium, 2-3.
[37]
Windsor, P., Yong, J. and Bell, M.C.-T. (2020) Accounting Standards and Insurer Solvency Assessment. International Monetary Fund. https://doi.org/10.2139/ssrn.3721187
[38]
Chiantera, M. (2023) Data Quality and Data Governance in Insurance Corporations Doctoral Dissertation, Politecnico di Torino.
[39]
Grebeck, M. and Rachev, S. (2005) Stochastic Programming Methods in Asset-Liability Management. Investment Management and Financial Innovations, 2, 82-90.
[40]
Diehl, M. (2023) Large Insurance Portfolios-Simulation, Strategy Decisions, Asset-Liability Management. Doctoral Dissertation, Universitt Kaiserslautern-Landau.
[41]
Chaim, R.M. (2010) Dynamic Asset and Liability Management. Pension Fund Risk Management, 129.
[42]
Luckner, W.R., Abbott, M.C., Backus, J.E., Sergio Benedetti, F.S.A., Fcia, M., Bergman, D., Cox, S.H., SholomFeldblum, F.S.A., Gilbert, C.L., Liu, X.L. and Lui, V.Y. (2003) SOA Professional Actuarial Specialty Guide: Asset-Liability Management. Society of Actuaries.
[43]
Cummins, J.D. and Doherty, N.A. (2006) The Economics of Insurance Intermediaries. Journal of Risk and Insurance, 73, 359-396. https://doi.org/10.1111/j.1539-6975.2006.00180.x
[44]
Clemente, G.P. and Marano, P. (2020) The Broker Model for Peer-to-Peer Insurance: An Analysis of Its Value. The Geneva Papers on Risk and Insurance-Issues and Practice, 45, 457-481. https://doi.org/10.1057/s41288-020-00165-8
[45]
Doherty, N.A. and Muermann, A. (2005) Insuring the Uninsurable: Brokers and Incomplete Insurance Contracts (No. 2005/24). CFS Working Paper.
[46]
Obalola, M.A. and Ukpong, M.S. (2022) Reinsurance and the Determinants of the Ceding Decision of Life Insurance Companies in Nigeria: An Empirical Analysis. Journal of Corporate Governance, Insurance, and Risk Management, 9, 169-189. https://doi.org/10.51410/jcgirm.9.1.11
[47]
Frees, E.W., Shi, P. and Valdez, E.A. (2009) Actuarial Applications of a Hierarchical Insurance Claims Model. ASTIN Bulletin: The Journal of the IAA, 39, 165-197. https://doi.org/10.2143/AST.39.1.2038061
[48]
Darcy, S.P. and Gorvett, R.W. (1998) A Comparison of Property/Casualty Insurance Financial Pricing Models. Proceedings of the Casualty Actuarial Society, 85, 1-88.
[49]
Mourdoukoutas, F., Pantelous, A.A. and Taylor, G. (2023) Competitive Insurance Pricing Strategies for Multiple Lines of Business: A Game Theoretic Approach. SSRN Electronic Journal, 1-46.
[50]
Abbott, W.M., Clarke, T.G. and Treen, W.R. (1981) Some Financial Aspects of a General Insurance Company. Journal of the Institute of Actuaries, 108, 119-209. https://doi.org/10.1017/S0020268100040725
