Academic Journal of Computing & Information Science, 2023, 6(4); doi: 10.25236/AJCIS.2023.060416.
Central University of Finance and Economics, Beijing, China
Travel insurance is a crucial component for any traveller as it offers protection against financial losses resulting from unforeseen events during a trip, such as trip cancellations, medical emergencies, lost luggage, and related issues. This study aims to investigate the potential of machine learning (ME) techniques for predicting the probability of travel insurance claims. In order to tackle the issue of managing extensive and intricate datasets, advanced statistical techniques were employed, including keyword extraction, feature extraction, and Chi-squared tests. Our evaluation of four popular ML models, namely balanced random forest (BRF), support vector machines (SVM), logistic regression (LR), and balanced bagging (BB), highlight that the BRF model outperforms the other models in predicting travel insurance claims. Our study emphasises the advantages of utilising machine learning algorithms in processing large datasets, producing predictions on future insurance claims, and adapting to changing circumstances, thus serving as a valuable tool for practitioners in the travel insurance industry.
machine learning, travel insurance claims, BRF, LR, SVM, balanced bagging
Xiaonan Li. Exploring the Potential of Machine Learning Techniques for Predicting Travel Insurance Claims: A Comparative Analysis of Four Models. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 4: 118-125. https://doi.org/10.25236/AJCIS.2023.060416.
 M. A. Rubi, M. H. I. Bijoy, S. Chowdhury, and M. K. Islam, "Machine Learning Prediction of Consumer Travel Insurance Purchase Behavior," in 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2022: IEEE, pp. 1-5.
 D. A. Hamzah, "Predicting travel insurance policy claim using logistic regression," Applied Quantitative Analysis, vol. 1, no. 1, pp. 1-7, 2021.
 P. A. Leggat and F. W. Leggat, "Travel insurance claims made by travelers from Australia," Journal of Travel Medicine, vol. 9, no. 2, pp. 59-65, 2002.
 J. T. Gamaliel and H. Murfi, "Regularization learning network for insurance claim prediction in travel insurance," Journal of Advanced Research in Dynamical and Control Systems, vol. 12, no. 4 Special Issue, pp. 1496-1503, 2020.
 C. Leboeuf, P. Gagnon, and M. M. Hébert, "Travel Insurance Claims modeling using Statistical Learning Mitacs Acceleration Research Project," 2020.
 Z. Quan and E. A. Valdez, "Predictive analytics of insurance claims using multivariate decision trees," Dependence Modeling, vol. 6, no. 1, pp. 377-407, 2018.
 G. Kerr and L. Kelly, "Travel insurance: the attributes, consequences, and values of using travel insurance as a risk-reduction strategy," Journal of Travel & Tourism Marketing, vol. 36, no. 2, pp. 191-203, 2019.
 I. M. Njoh-Paul, "A Comparative Study of Ensemble Techniques and Individual Classifiers in Predicting Insurance Claim," Dublin, National College of Ireland, 2020.
 A. Al Mamun, M. K. Rahman, Q. Yang, T. Jannat, A. A. Salameh, and S. A. Fazal, "Predicting the willingness and purchase of travel insurance during the COVID-19 pandemic," Frontiers in public health, vol. 10, 2022.
 R. F. Grace and D. Penny, "Travel insurance and medical evacuation: view from the far side," Medical journal of Australia, vol. 180, no. 1, pp. 32-35, 2004.
 N. Zaitseva and L. Chernikova, "Features and Prospects in the Development of the Services Provided in the Field of Travel Insurance," Middle East Journal of Scientific Research, vol. 16, no. 7, pp. 996-1002, 2013.
 L. Jia-lan, W. Xiao-yu, Y. Wan-jun, W. Zi-chen, Z. Huai-lin, and C. Nai-meng, "Research and design of travel insurance system based on blockchain," in 2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), 2019: IEEE, pp. 121-124.
 F. B. Cappon, "Travel Insurance: The Urgent Need For Improved Regulation," 2014.
 Z. Rybák, "Analysis of the individual travel insurance in the Czech Republic," European Financial and Accounting Journal, vol. 13, no. 2, pp. 5-30, 2018.
 S. Rawat, A. Rawat, D. Kumar, and A. S. Sabitha, "Application of machine learning and data visualization techniques for decision support in the insurance sector," International Journal of Information Management Data Insights, vol. 1, no. 2, p. 100012, 2021.
 S. K. Vandrangi, "Predicting The Insurance Claim By Each User Using Machine Learning Algorithms," Journal of Emerging Strategies in New Economics, vol. 1, no. 1, pp. 1-11, 2022.
 P. C. Verhoef and B. Donkers, "Predicting customer potential value an application in the insurance industry," Decision support systems, vol. 32, no. 2, pp. 189-199, 2001.
 A. Panchapakesan, R. Abielmona, R. Falcon, and E. Petriu, "Prediction of container damage insurance claims for optimized maritime port operations," in Advances in Artificial Intelligence: 31st Canadian Conference on Artificial Intelligence, Canadian AI 2018, Toronto, ON, Canada, May 8–11, 2018, Proceedings 31, 2018: Springer, pp. 265-271.
 C. A. Duah, "Predictive Modeling of Insurance Claims Using Reversible Jump Markov Chain Monte Carlo Methods," 2017.
 N. Nawaratana, "Analysis of distributions for insurance claims data," School of Mathematics Institute of Science Suranaree University of Technology, 2019.
 J.-M. Kim, J. Kim, and I. D. Ha, "Application of Deep Learning and Neural Network to Speeding Ticket and Insurance Claim Count Data," Axioms, vol. 11, no. 6, p. 280, 2022.
 A. J. Dey and H. K. D. Sarma, "A Survey on application of machine learning in property and casualty insurance," in Contemporary Issues in Communication, Cloud and Big Data Analytics: Proceedings of CCB 2020, 2022: Springer, pp. 307-314.
 I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, "Machine learning and data mining methods in diabetes research," Computational and structural biotechnology journal, vol. 15, pp. 104-116, 2017.
 R. P. Bellapu, S. Mylsamy, S. R. Krishna, and P. Kundu, "Developing and evaluation of machine learning models in the insurance sector," 2021.
 N. Sharma, S. K. Gautam, A. A. Henry, and A. Kumar, "Application of big data and machine learning," Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications, pp. 305-333, 2020.
 M. Riikkinen, H. Saarijärvi, P. Sarlin, and I. Lähteenmäki, "Using artificial intelligence to create value in insurance," International Journal of Bank Marketing, 2018.
 S. Tober, "Tree-based Machine Learning Models with Applications in Insurance Frequency Modelling," ed, 2020.
 K. P. Sinha, M. Sookhak, and S. Wu, "Agentless Insurance Model Based on Modern Artificial Intelligence," in 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI), 2021: IEEE, pp. 49-56.
 S. Meng, Y. Gao, and Y. Huang, "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, vol. 106, pp. 115-127, 2022.
 M. Eling, D. Nuessle, and J. Staubli, "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance-Issues and Practice, pp. 1-37, 2021.
 B. Ljubic, Advanced Machine Learning Models in Prediction of Medical Conditions. Temple University, 2021.