New Information Technology Data Have Been Reported by Investigators at University of Tehran (Proposing a Novel Data-driven Optimization Methodology To Calculate the Insurance Premium In the Iranian Health Insurance Industry): Information Technology

2023 AUG 07 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Investigators discuss new findings in Information Technology. According to news reporting out of Tehran, Iran, by NewsRx editors, research stated, “This study aims to manage the two most common and critical disruptions of Iranian health insurance (declining market share and errors in predicting the indemnities) by proposing a novel data-driven methodology for calculating its insurance premium. Here, using the optimal machine learning algorithm selected using a Bayesian best-worst method, insurers are classified based on their preparedness for causing disruptions.”

Our news journalists obtained a quote from the research from the University of Tehran, “Then, the indemnity of each group of insureds is predicted. Finally, the appropriate premium for each group of insureds is calculated separately using a new mathematical optimization model.”

According to the news editors, the research concluded: “The results of our real-life case study guarantee the insurer’s profitability and reduction of its bankruptcy risk even by announcing lower premiums.”

This research has been peer-reviewed.

For more information on this research see: Proposing a Novel Data-driven Optimization Methodology To Calculate the Insurance Premium In the Iranian Health Insurance Industry. Emerging Markets Finance and Trade, 2023. Emerging Markets Finance and Trade can be contacted at: Routledge Journals, Taylor & Francis Ltd, 2-4 Park Square, Milton Park, Abingdon OX14 4RN, Oxon, England.

Our news journalists report that additional information may be obtained by contacting Reza Tavakkoli-Moghaddam, University of Tehran, College of Engineering, School of Industrial Engineering, Tehran, Iran. Additional authors for this research include Mohammad Alipour-Vaezi and Kamran Rezaie.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1080/1540496X.2023.2218963. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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