Statistical Learning for Predictive Modeling of Auto Insurance Claims
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The auto insurance branch is considered the principal and the first activity in an insurance company in terms of the claims paid and the premiums collected. For that, the insurers deal with a dilemma. They must price their automobile insurance products at their fair value, and at the same time, they must take profit from this pricing. Thus, pricing is a central issue for insurance companies, especially auto insurance. In the same way, the reinsurance companies care about the pricing of the claims transferred by the insurance companies for the purpose of making a profit. In this context, this article aims to model the costs and frequency of claims associated with the automobile insurance and reinsurance contracts, notably the pricing of the automobile liability guaranty, with a view of helping the actuary to get better pricing of reinsurance treaties, particularly "excess of loss" treaties using a new method. The approach to this work consists of the use of machine learning algorithms as an alternative to conventional methods. The statistical learning models are trained and tested on a large data-set that contains the explanatory variables. The results of this study demonstrate the advantage of applying statistical learning in the pricing of auto insurance and reinsurance contracts based on the predicted cost and the predicted frequency of the insured’ claims. The new mechanism presented in this paper is vital for reinsurance companies to get accurate charge pricing.
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M. David, Auto Insurance Premium Calculation Using Generalized Linear Models, Procedia Economics and Finance, vol. 20, pp. 147-156, Jan. 2015.
X. Milhaud, Segmentation and modeling of redemption behavior in Life Insurance, Actuary dissertation, Lyon university, France, 2011. Accessed: Jun. 16, 2022. [Online]. Available:
P. Piette, Contributions of statistical learning to actuarial science and financial risk management, PhD. dissertation, Lyon university, France, 2019. Accessed: Jun. 16, 2022. [Online]. Available:
M. Azzone, E. Barucci, G. Giuffra Moncayo, and D. Marazzina, A machine learning model for lapse prediction in life insurance contracts, Expert Systems with Applications, vol. 191, p. 116261, Apr. 2022.
G. Zhang, X. Zhang, M. Bilal, W. Dou, X. Xu, and J. J. P. C. Rodrigues, Identifying fraud in medical insurance based on blockchain and deep learning, Future Generation Computer Systems, vol. 130, pp. 140-154, May 2022.
N. Meraihi, Modeling claims frequency with telematics data using the GBMP model, Master dissertation, Quebec university, Montreal, Quebec, Canada, 2019.
S. Tober, Tree-based machine learning models with applications in insurance frequency modelling. 2020. Accessed: Aug. 31, 2021. [Online]. Available:
El Attar, A., El Hachloufi, M., Guennoun, Z., The Optimization of Reinsurance by the Maximization of Technical Benefits and Minimization of Probability of Ruin Using Genetic Algorithms, (2016) International Review on Modelling and Simulations (IREMOS), 9 (1), pp. 18-28.
N. Kotsalo, Machine learning methods vs. traditional methods in forecasting loss reserves, Master dissertation, Arcada University of Applied Sciences, Finlande, 2021. Accessed: Aug. 31, 2021. [Online]. Available:
F. D. June, Gradient boosting techniques for individual loss reserving in non life insurance, Master dissertation, Quebec University, Montreal, Quebec, Canada, 2019.
M. Baudry and C. Y. Robert, A machine learning approach for individual claims reserving in insurance, Applied Stochastic Models in Business and Industry, vol. 35, no. 5, pp. 1127-1155, 2019.
E. Dankwa, Individual loss reserving in general insurance using neural networks, Master dissertation, Quebec University, Montreal, Quebec, Canada, 2020.
F. Jabiri, Applications of unsupervised classification methods to anomaly detection, Master dissertation, Laval university, Quebec, Canada, 2020. Accessed: Aug. 31, 2021. [Online]. Available:
S. Zhao, Machine learning methods for the detection of fraudulent insurance claims, Master dissertation, Concordia University, Montreal, Quebec, Canada, 2020.
Y.-L. Grize, W. Fischer, and C. Lützelschwab, Machine learning applications in non life insurance, Applied Stochastic Models in Business and Industry, vol. 36, no. 4, pp. 523-537, 2020.
A. Charpentier and M. Denuit, Mathematics of non-life insurance. Volume 1, Fundamental principles of risk theory, Economica. 2004. Accessed: Sep. 30, 2022. [Online]. Available:
A. Charpentier and M. Denuit, Mathematics of non-life insurance: Volume 2, Pricing and provisioning. Paris: Economica, 2005.
P. Li, M. Zhou, and D. Yao, Optimal time for the excess of loss reinsurance with fixed costs, International Review of Economics & Finance, vol. 79, pp. 466-475, May 2022.
Masoud, M., Jaradat, Y., Alsakarnah, R., A Non-Content Multilayers Hybrid Machine Learning Web Phishing Detection Model, (2022) International Review on Modelling and Simulations (IREMOS), 15 (2), pp. 108-115.
Bataineh, A., Batayneh, W., Harahsheh, T., Hijazi, K., Alrayes, A., Olimat, M., Bataineh, A., Early Detection of Cardiac Diseases from Electrocardiogram Using Artificial Intelligence Techniques, (2021) International Review on Modelling and Simulations (IREMOS), 14 (2), pp. 128-136.
Karlov, D., Prokazov, I., Bakshtanin, A., Matveeva, T., Kondratenko, L., Optimizing Neural Network Model Performance for Wind Energy Forecasting, (2021) International Review on Modelling and Simulations (IREMOS), 14 (3), pp. 185-193.
Shirasuna, M., Identification of Sleep Apnea Syndrome by Analyzing Sleep Sound Data Using a Clustering Method, (2020) International Journal on Engineering Applications (IREA), 8 (3), pp. 118-124.
Triqui, B., Benyettou, A., Semi-Supervised Kohonen Map for Cardiac Anomalies Detection, (2019) International Review on Modelling and Simulations (IREMOS), 12 (3), pp. 196-205.
Emadi, S., Emadi, S., Analyzing Cost and Time Objectives in the Construction Projects Using Artificial Neural Network, (2022) International Review of Civil Engineering (IRECE), 13 (2), pp. 91-98.
Zhang, S., Sakulyeva, T., Pitukhin, E., Doguchaeva, S., Neuro-Fuzzy and Soft Computing - A Computational Approach to Learning and Artificial Intelligence, (2020) International Review of Automatic Control (IREACO), 13 (4), pp. 191-199.
Pinzón-Arenas, J., Jiménez-Moreno, R., Pachón-Suescún, C., Handwritten Word Searching by Means of Speech Commands Using Deep Learning Techniques, (2019) International Review on Modelling and Simulations (IREMOS), 12 (4), pp. 253-263.
Abidi, M., Fizazi, H., Boudali, N., Clustering of Remote Sensing Data Based on Spherical Evolution Algorithm, (2021) International Review of Aerospace Engineering (IREASE), 14 (2), pp. 72-79.
Idroes, R., Noviandy, T., Maulana, A., Suhendra, R., Sasmita, N., Muslem, M., Idroes, G., Kemala, P., Irvanizam, I., Application of Genetic Algorithm-Multiple Linear Regression and Artificial Neural Network Determinations for Prediction of Kovats Retention Index, (2021) International Review on Modelling and Simulations (IREMOS), 14 (2), pp. 137-145.
Bou Nassif, A., Soudan, B., Azzeh, M., Attilli, I., Almulla, O., Artificial Intelligence and Statistical Techniques in Short-Term Load Forecasting: a Review, (2021) International Review on Modelling and Simulations (IREMOS), 14 (6), pp. 408-430.
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