This Python program simulates the total annual loss distribution for an insurance portfolio using a Monte Carlo approach. It models the number of claims per year using a Poisson distribution and the severity of each claim using a Lognormal distribution. For each simulation iteration, it samples a number of claims, generates random loss amounts for those claims, sums them to calculate the total loss for the year, and repeats this process many times to build an empirical distribution of total losses. The program then calculates key risk metrics such as Value at Risk (VaR) and Tail Value at Risk (TVaR) at specified confidence levels, and visualizes the resulting loss distribution using a histogram. This provides insight into the range and likelihood of potential losses, including rare but extreme outcomes.