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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. 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. The results of our real-life case study guarantee the insurer’s profitability and reduction of its bankruptcy risk even by announcing lower premiums.

Proposing a Novel Data-Driven Optimization Methodology to Calculate the Insurance Premium in the Iranian Health Insurance Industry