| Summary: | In a health insurance portfolio, not all policyholders possess similar risk levels; specific individuals exhibit higher risk than others. Consequently, it might appear inequitable to impose the same premium on everyone. This diversity can be mitigated by employing risk categories that exhibit greater uniformity, considering factors such as gender, age, and other indicators. By applying risk classification, the expected cost for each risk category can be estimated using predefined methods. This study introduces an approach for categorizing insured individuals in health insurance based on statistical learning, specifically employing the multinomial logistic regression algorithm. The research underscores the significance of risk classification in establishing an equitable pricing structure.
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