The simplest case for simulation. Let's say you have a forecasting model for the number of loan defaults, you also have a model for losses on defaulted loans. Now you need to forecast the total loss which the product of defaults and losses given the default. You can't simply multiply the defaults and losses on defaults to get the confidence intervals of total loss.

The reason is that if you have a random variable $x_i$, of which you know the densities, it doesn't mean that you can easily get the density of the product $x_1\cdot x_2$. On the other hand, if you know the correlation between numbers, it's easy to simulate the correlated numbers, and get the simulated distribution of losses.

The simplest case for simulation. Let's say you have a forecasting model for the number of loan defaults, you also have a model for losses on defaulted loans. Now you need to forecast the total loss which the product of defaults and losses given the default. You can't simply multiply the defaults and losses on defaults to get the confidence intervals of total loss.

The reason is that if you have a random variable $x_i$, of which you know the densities, it doesn't mean that you can easily get the density of the product $x_1\cdot x_2$. On the other hand, if you know the correlation between numbers, it's easy to simulate the correlated numbers, and get the simulated distribution of losses.

This paper has a MBA-level description of this use case for operation risk estimation, where you have the distributions of loss frequency and amounts, and combine them to get the total loss distribution.