The article explores how copulas can be used to understand the relationship between variables in insurance data. By analyzing real-life insurance data, the researchers found that certain types of copulas with heavy tail dependence are effective for modeling insurance claim data. This approach helps predict losses in insurance and finance by identifying the best copula for each case.