@ARTICLE{Jahanbani, author = {Jahanbani, Ehsan and Payandeh Najafabadi, Amirteymor and Masoumifard, Khaled and }, title = {A Credibility Approach to Predicting Healthcare Costs: A Case Study of Dental Costs}, volume = {5}, number = {2}, abstract ={Introduction: The increase in the cost of medical services and the aging of Iran's population have created problems for the medical insurance industry, which has turned this field into one of the most risky fields. In this situation, one of the most basic needs of this field is to fit a suitable model to the treatment losses, so that the insurer can check the trend of the treatment costs, have an accurate estimate of the next year's losses, and calculate the corresponding insurance premium. Methods: Based on an empirical fundamental study, the loss data related to group treatment insurance policies issued from the middle of 2017 to the middle of 2019 was collected by Day insurance company and using mixed distributions, dental claims were modeled and the loss of the next year was predicted using the credibility method. Results: Due to the heterogeneity in the data, mixed distribution will be more suitable due to its high flexibility. The insured people were divided into 2 high-risk and low-risk categories according to their characteristics, and a distribution was given to each of these categories. The general distribution of damages is considered as a mixed distribution of these distributions, and the mixed weights of this distribution were estimated using logistic regression. Conclusion: One of the most basic issues in all types of insurances is determining their insurance premiums. In this research, using the theory of credibility, the next year's insurance premium is calculated for each of the insured according to their risk parameter. In fact, credibility theory combines the existing insurance premium with past losses and provides the adjusted premium. }, URL = {http://journal.ihio.gov.ir/article-1-229-en.html}, eprint = {http://journal.ihio.gov.ir/article-1-229-en.pdf}, journal = {Iranian Journal of Health Insurance}, doi = {}, year = {2022} }