This paper describes a framework for a statistical anomaly prediction system using Quickprop neural network forecasting model, which predicts unauthorized invasions of user based on previous observations and takes further action before intrusion occurs. The experimental study is performed using real data provided by a major Corporate Bank. A comparative evaluation of the Quickprop neural network over the traditional neural network models was carried out using mean absolute percentage error on a prediction data set and a better prediction accuracy has been observed. Further, in order to make a legitimate comparison, the dataset was divided into two statistically equivalent subsets, viz. the training and the prediction sets, using genetic algorithm.