Abstract
Finding a proper insurance package becomes a challenging issue for new customers due to the variety of insurance packages and many factors from both insurance packages’ policies and users’ profiles for considering. This paper introduces a recommender model named INSUREX that attempts to analyze historical data of application forms and contact documents. Then, machine learning techniques based on item-attribute-value prediction are adopted to find out the pattern between attributes of insurance packages. Next, our recommender model suggests several relevant packages to users. The measurement of the model results in high performance in terms of HR@K and F1-score. In addition, a web-based proof-of-concept application has been developed by utilizing the INSUREX model in order to recommend insurance packages and riders based on a profile from the user input. The evaluation against users demonstrates that the recommender model helps users get start in choosing right insurance plans.
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References
Cappiello, A.: Technology and the Insurance Industry: Re-configuring the Competitive Landscape. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-74712-5
Hinduja, A., Pandey, M.: An intuitionistic fuzzy AHP based multi criteria recommender system for life insurance products. Int. J. Adv. Stud. Comput. Sci. Eng. 7(1), 1–8 (2018)
Sahoo, S., Ratha, B.K.: Recommending life insurance using fuzzy multi criteria decision-making. Int. J. Pure Appl. Math. 118(16), 735–759 (2018)
Archenaa, J., Anita, E.M.: Health recommender system using big data analytics. J. Manage. Sci. Bus. Intell. 2(2), 17–24 (2017)
Qazi, M., Tollas, K., et al.: Designing and deploying insurance recommender systems using machine learning. In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, p. e1363 (2020)
Desirena, G., Diaz, A., et al.: Maximizing customer lifetime value using stacked neural networks: an insurance industry application. In: 2019 18th IEEE International Conference on Machine Learning And Applications (ICMLA) (2019)
Kang, J., Sim, K.M.: Towards agents and ontology for cloud service discovery. In: 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (2011)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chawla, N.V., Bowyer, K.W., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
iTAX. https://www.itax.in.th/market. Accessed 16 Aug 2020
Zhou, X., Yue, X., et al.: The state-of-the-art in personalized recommender systems for social networking. Artif. Intell. Rev. 37(2), 119–132 (2012)
de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-aware content-based recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 119–159. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_4
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Chawuthai, R., Choosak, C., Weerayuthwattana, C. (2021). A Recommender System for Insurance Packages Based on Item-Attribute-Value Prediction. In: Hong, TP., Wojtkiewicz, K., Chawuthai, R., Sitek, P. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2021. Communications in Computer and Information Science, vol 1371. Springer, Singapore. https://doi.org/10.1007/978-981-16-1685-3_7
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DOI: https://doi.org/10.1007/978-981-16-1685-3_7
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