This paper investigates insurance pricing using frequency-severity models, focusing on the Potential Deviation Ratio (PDR) as a key measure in premium determination. The objective is to develop an accurate and actuarially sound pricing approach by modeling claim frequency and severity using appropriate statistical distributions. Specifically, we propose that the product of two fitted distributions—one for frequency and the other for severity—can be used to calculate the pure premium.

The methodology includes fitting various distributions to frequency and severity data from a major Iranian insurance company. To model the dependency structure, we employ the Clayton copula. This dependency structure is used when calculating pure premium by multiplying frequency and severity samples based on the fitted distributions. Kolmogorov-Smirnov, Anderson-Darling, and Chi-Squared tests are applied to evaluate model performance.

The results demonstrate that using the pure premium, based on their respective fitted distributions, significantly enhances the accuracy of the model. This approach leads to more precise risk classification, improved premium setting, and ultimately, fairer pricing strategies. Additionally, the findings indicate that incorporating dependency structures between frequency and severity not only improves risk assessment but also contributes to greater financial stability for insurers.