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The insurance industry is an industry directly related to risk. The risk that occurs due to the claim severity that must be paid by an insurance company. In practice, claim severity come from various sources of the company’s business so that it is seen as an aggregate risk. In this article, aggregate risk is constructed from the sum of the two claim severities of dependent line of business. Dependence between claim severities can be represented through Copula. Copula used in this article are Copula Clayton, Frank Copula, and Copula Gumbel. Then, claim severity must be modeled and predictable in value so the company can carry out a strategy to prevent bankruptcy. TheAutoregressive Conditional Amount (ACA) model is a model that can be used to model claim severity by utilizing data from the past. Furthermore, the Value-at-Risk (VaR) method can be used to predict the value of future claims with a certain level of confidence. By utilizing the Copula dependency model, the ACA claim model (1,1) and the Value-at-Risk method, the VaR prediction for aggregate risk is obtained. The prediction result is 13 with the best dependency model using Copula Gumbel.

Prediksi Ukuran Risiko Agregat Klaim Berbasis Copula pada Model Autoregressive Conditional Amount (ACA)