Data mining in fund raising applications have been shown to significantly increase funds raised by charity organisations. This research investigates the accuracy of statistical classification techniques when applied to various prediction problems in fund raising. The results show that increased accuracy of predictions can be achieved by using actions taken by fund raisers as attributes as well as donor profiles. Where classification techniques fail, data mining results are shown to be useful for formulating and solving optimisation problems which are solved to provide the best course of actions for maximum return on investment.