Abstract
Like the other financial markets, insurance markets need to increase their profit margins if they want to continue their activities. In this article, we examine the factors that can affect the costs of insurance companies and examine the relationships between these factors. We use two approaches to determine the effect of some variables on the charge of insurance companies. Our first approach is to use data analysis. Then we analyzed the relationships between these variables using diagrams. We used statistical graphs to examine the effect of each variable on the cost of insurance companies. Then we determine the relationships between these variables and the trade of these variables on each other. Another approach we use is the Best–Worst method that is one of multi-criteria decision-making techniques. Our expert finds each variable's weight on the charge of insurance companies by using the Best–Worst method. We implemented these two methods on an insurance company's data, and we showed which variable can have the most impact on costs. These results can help insurance companies to determine macro-fiscal policies and pricing. Using these results, insurance companies can divide their customers into several sections and offer the price of their services to each insured separately.
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References
Anom B (2020) Ethics of Big Data and artificial intelligence in medicine. Ethics Med Public Health 15:100568
Apergis N, Poufinas T (2020) The role of insurance growth in economic growth: Fresh evidence from a panel of OECD countries. N Am J Econ Finance 50:101217
Chae YM, Ho SH, Cho KW, Lee DH, Ji SH (2001) Data mining approach to policy analysis in a health insurance domain. Int J Med Inf 62(2):103–111. https://doi.org/10.1016/S1386-5056(01)00154-X
Devale A, Kulkarni R (2012) Applications of data mining techniques in life insurance. Int J Data Min Knowl Manag Process 2(4):31–40
dos Santos JRR, Dias CM, Filho AC (2021) Machine learning and national health data to improve evidence: Finding segmentation in individuals without private insurance. Health Policy Technol 10(1):79–86. https://doi.org/10.1016/j.hlpt.2020.11.002
Dotoli M, Epicoco N, Falagario M (2020) Multi-criteria decision making techniques for the management of public procurement tenders: a case study. Appl Soft Comput 88:106064. https://doi.org/10.1016/j.asoc.2020.106064
Dutt R (2020) The impact of artificial intelligence on healthcare insurances. Artificial intelligence in healthcare. Elsevier, Amsterdam, pp 271–293
Faizi S, Sałabun W, Nawaz S, Rehman AU, Watróbski J (2021) Best–worst method and Hamacher aggregation operations for intuitionistic 2-tuple linguistic sets. Expert Syst Appl 181:115088. https://doi.org/10.1016/j.eswa.2021.115088
Fei L, Lu J, Feng Y (2020) An extended best-worst multi-criteria decision-making method by belief functions and its applications in hospital service evaluation. Comput Ind Eng 142:106355. https://doi.org/10.1016/j.cie.2020.106355
Gan W, Lin JCW, Chao HC (2017) Data mining in distributed environment: a survey, Wiley interdisciplinary reviews. Data Min Knowl Disco 7(6):1–19
Ghasemi S, Aghsami A, Rabbani M (2020) Data envelopment analysis for estimate efficiency and ranking operating rooms: a case study. Int J Res Ind Eng 10(1):67–86
Gouareh A, Settou B, Settou N (2021) A new geographical information system approach based on best worst method and analytic hierarchy process for site suitability and technical potential evaluation for large-scale CSP on-grid plant: an application for Algeria territory. Energy Convers Manage 235:113963. https://doi.org/10.1016/j.enconman.2021.113963
Gupta H, Barua MK (2018) A framework to overcome barriers to green innovation in SMEs using BWM and Fuzzy TOPSIS. Sci Total Environ 633:122–139
Hu L, Li L, Ji J (2020) Machine learning to identify and understand key factors for provider-patient discussions about smoking. Prev Med Rep 20:101238. https://doi.org/10.1016/j.pmedr.2020.101238
Jamsheela O, Raju G (2015) Frequent itemset mining algorithms: a literature survey. In: 2015 IEEE international advance computing conference (IACC), 2015, pp 1099–1104. https://doi.org/10.1109/IADCC.2015.7154874
Jashma Suresh PP, Dinesh Acharya U, Subba Reddy NV (2021) Study of effective mining algorithms for frequent itemsets. In: Hemanth J, Bestak R, Chen JIZ (eds) Intelligent data communication technologies and internet of things. Lecture notes on data engineering and communications technologies, vol 57. Springer, Singapore. https://doi.org/10.1007/978-981-15-9509-7_41
Jayaraman I, Panneerselvam AS (2021) A novel privacy preserving digital forensic readiness provable data possession technique for health care data in cloud. J Ambient Intell Human Comput 12(5):4911–4924. https://doi.org/10.1007/s12652-020-01931-1
Karuppiah SV, Gurunathan G (2021) Secured storage and disease prediction of E-health data in cloud. J Ambient Intell Human Comput 12(6):6295–6306. https://doi.org/10.1007/s12652-020-02205-6
Kaya İ, Çolak M, Terzi F (2019) A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energ Strat Rev 24:207–228. https://doi.org/10.1016/j.esr.2019.03.003
Kelley KH, Fontanetta LM, Heintzman M, Pereira N (2018) Artificial intelligence: Implications for social inflation and insurance. Risk Manag Insur Rev 21(3):373–387
Kirlidog M, Asuk C (2012) A fraud detection approach with data mining in health insurance. Procedia Soc Behav Sci 62:989–994. https://doi.org/10.1016/j.sbspro.2012.09.168
Kolat D, Ajlan Kökçü H, Kiranli M, Özbiltekin M, Öztürkoğlu Y (2020) Measuring service quality in the logistic sector by using servqual and best worst method. Springer, Cham
Li X, Li D, Deng Y, Xing J (2021) Intelligent mining algorithm for complex medical data based on deep learning. J Ambient Intell Human Comput 12(2):1667–1678. https://doi.org/10.1007/s12652-020-02239-w
Lin C-W, Djenouri Y, Srivastava G (2021a) Efficient closed high-utility pattern fusion model in large-scale databases. Inf Fusion 76:122–132
Lin JC-W, Djenouri Y, Srivastava G, Yun U, Fournier-Viger P (2021b) A predictive GA-based model for closed high-utility itemset mining. Appl Soft Comput 108:107422. https://doi.org/10.1016/j.asoc.2021.107422
Liu P, Zhu B, Seiti H, Yang L (2021) Risk-based decision framework based on R-numbers and best-worst method and its application to research and development project selection. Inf Sci 571:303–322. https://doi.org/10.1016/j.ins.2021.04.079
Ma W, Li X, Wang X (2021) Water Saving Management Contract, identification and ranking of risks based on life cycle and best-worst method. J Clean Prod 306:127153. https://doi.org/10.1016/j.jclepro.2021.127153
Mehta N, Pandit A, Shukla S (2019) Transforming healthcare with big data analytics and artificial intelligence: a systematic mapping study. J Biomed Inf 100:103311
Mohammadnazari Z, Ghannadpour SF (2018) Employment of multi criteria decision making techniques and mathematical formulation for construction of the sustainable hospital. Int J Hosp Res 7(2):112–127
Mohammadnazari Z, Ghannadpour SF (2021) Sustainable construction supply chain management with the spotlight of inventory optimization under uncertainty. Environ Dev Sustain 23(7):10937–10972
Mohtashami A (2021) A novel modified fuzzy best-worst multi-criteria decision-making method. Expert Syst Appl 181:115196. https://doi.org/10.1016/j.eswa.2021.115196
Neto JC, Filipe JA, Caleiro AB (2019) Creativity and innovation: a contribution of behavioral economics. Int J Innov Stud 3(1):12–21
Owadally I, Zhou F, Otunba R, Lin J, Wright D (2019) An agent-based system with temporal data mining for monitoring financial stability on insurance markets. Expert Syst Appl 123:270–282. https://doi.org/10.1016/j.eswa.2019.01.049
Pramanik MI, Lau RY, Azad MA, Hossain MS, Chowdhury MK, Karmaker BK (2020) Healthcare informatics and analytics in big data. Expert Syst Appl 152:113388
Ren J (2018) Technology selection for ballast water treatment by multi-stakeholders: a multi-attribute decision analysis approach based on the combined weights and extension theory. Chemosphere 191:747–760
Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57
Rezaei J, van Roekel WS, Tavasszy L (2018) Measuring the relative importance of the logistics performance index indicators using best worst method. Transp Policy 68:158–169. https://doi.org/10.1016/j.tranpol.2018.05.007
Salimi N, Rezaei J (2018) Evaluating firms’ R&D performance using best worst method. Eval Progr Plann 66:147–155
Shuibul Qarnain S, Muthuvel S, Bathrinath S (2021) Modelling of driving factors for energy efficiency in buildings using Best Worst Method. Mater Today Proc 39:137–141. https://doi.org/10.1016/j.matpr.2020.06.400
Singh K, Swarnakar V, Singh AR (2021) Lean six sigma project selection using best worst method. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.04.094
Sinthuja M, Evangeline D, Raja SP, Shanmugarathinam G (2022) Frequent itemset mining algorithms—a literature survey. In: Raj JS, Palanisamy R, Perikos I, Shi Y (eds) Intelligent sustainable systems, vol 213. Lecture Notes in Networks and Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_13
Smith KA, Willis RJ, Brooks M (2000) An analysis of customer retention and insurance claim patterns using data mining: a case study. J Oper Res Soc 51(5):532–541
Tavakkoli-Moghaddam R, Alipour-Vaezi M, Mohammad-Nazari Z (2020) A new application of coordination contracts for supplier selection in a cloud environment. In: Lalic B, Majstorovic V, Marjanovic U, von Cieminski G, Romero D (eds) Advances in production management systems. Towards smart and digital manufacturing. APMS 2020. IFIP advances in information and communication technology, vol 592. Springer, Cham. https://doi.org/10.1007/978-3-030-57997-5_23
Wang HD (2020) Research on the features of car insurance data based on machine learning. Procedia Comput Sci 166:582–587. https://doi.org/10.1016/j.procs.2020.02.016
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Mamoudan, M.M., Forouzanfar, D., Mohammadnazari, Z. et al. Factor identification for insurance pricing mechanism using data mining and multi criteria decision making. J Ambient Intell Human Comput 14, 8153–8172 (2023). https://doi.org/10.1007/s12652-021-03585-z
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DOI: https://doi.org/10.1007/s12652-021-03585-z