The article explores a new method to analyze risks by estimating claim amounts without knowing their exact distribution. By using a nonparametric approach called kernel density estimation, the researchers compared different ways to estimate claim amounts in real-world scenarios. Their findings show that this method can help approximate the stability of risk models accurately, even when the distribution of claim amounts is unknown.