Insurers are required to have buffers to be able to meet financial obligations that result from their portfolios, which are determined using a cash flow model. The input of such a cash flow model consists among of things, of two mortality tables and the portfolio of an insurer. Mortality rates are simulated using the Lee-Carter model. These simulated rates are in turn used to simulate the cash flow corresponding to a portfolio. This results in one possibility of incoming and outgoing money over a period of time. Lots of simulations are required to get a reliable estimate for the future cash flow which is (depending on the number of simulations) computationally heavy and therefore time consuming. The calculation time is decreased by applying an extreme gradient boosting (XGBoost) machine learning method in which cash flows are considered target variables and the mortality tables are considered features of the model. The trained XGBoost model can predict the cash flows based on the mortality tables. The standard XGBoost model is extended to a multi-target regression model which is able to predict multiple target variables at once. This XGBoost model reduces the computation time and ensures that 99.5% of the predictions deviates within either 1% or 0.5% of the observed values. XGBoost gives a good method of determining a reliable estimate of the future cash flow.