Improving the purchasing power of customers for buying insurance portfolios based on artificial intelligence technology.
محورهای موضوعی : Operation Researchبهمن بابازاده بلوچی 1 , Kambiz Shahroodi 2 , سید مظفر میربرگ کار 3 , فرزین فرحبد 4
1 - دانشگاه آزاد اسلامی
2 - Department of Business Management, Faculty of Management & Accounting, Islamic Azad University of Rasht, Iran.
3 - استادیار گروه مدیریت، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.
4 - گروه مدیریت دانشگاه آزاد اسلامی واحد رشت،گیلان
کلید واژه: portfolio purchase, insurers' investment, artificial intelligence,
چکیده مقاله :
The main goal of this research is to design a model for Improving the purchasing power of customers for buying insurance portfolios based on artificial intelligence technology. The research method used is objective, applied, and descriptive-survey in terms of the method of collecting field data and in terms of the control of variables, and also because of the presentation of the model, it is an exploratory research. In this research, the insurance assets purchased by insurers in the insurance industry were first identified by reviewing previous researches. In this study, the association rule technique was used and the algorithm that presented the largest number of rules with high accuracy was selected. In this study, we used a set of data analysis, model design, and implementation methods. In this study, transactions related to cooperative insurance customers were studied. According to the results obtained, the performance of the Apriori and Fp-growth algorithms was evaluated based on execution time. The Apriori algorithm spent more execution time compared to the Fp-growth algorithm due to continuous scanning of the data set. The time required to identify patterns in the cooperative insurance customer data set based on different confidence level indicators in the Apriori and Fp-growth algorithms clearly shows that Apriori requires more execution time in large data sets, and this factor makes this software slow and costly (in terms of computation). This algorithm requires extensive memory for storage, which is considered a necessary problem because it creates problems for cooperative insurance, resulting in fewer transactions that can be calculated, and this factor is considered a major weakness.
The main goal of this research is to design a model for Improving the purchasing power of customers for buying insurance portfolios based on artificial intelligence technology. The research method used is objective, applied, and descriptive-survey in terms of the method of collecting field data and in terms of the control of variables, and also because of the presentation of the model, it is an exploratory research. In this research, the insurance assets purchased by insurers in the insurance industry were first identified by reviewing previous researches. In this study, the association rule technique was used and the algorithm that presented the largest number of rules with high accuracy was selected. In this study, we used a set of data analysis, model design, and implementation methods. In this study, transactions related to cooperative insurance customers were studied. According to the results obtained, the performance of the Apriori and Fp-growth algorithms was evaluated based on execution time. The Apriori algorithm spent more execution time compared to the Fp-growth algorithm due to continuous scanning of the data set. The time required to identify patterns in the cooperative insurance customer data set based on different confidence level indicators in the Apriori and Fp-growth algorithms clearly shows that Apriori requires more execution time in large data sets, and this factor makes this software slow and costly (in terms of computation). This algorithm requires extensive memory for storage, which is considered a necessary problem because it creates problems for cooperative insurance, resulting in fewer transactions that can be calculated, and this factor is considered a major weakness.
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