What Was The Problem?
A complete understanding of personal finances is becoming increasingly important as the average person’s disposable income has decreased due to a changing financial climate. The aim of this research proposal is to gather and analyze the existing personal expense prediction systems and their failures then finally build a personal expense prediction model using predictive analytics that will solve all of their shortcomings. This is split into two halves, accessing historical information in an easy way to understand and using machine learning techniques to predict future financial transactions. The security considerations of storing personal financial information are also considered. This begins with a review of the existing commercial personal finance applications and the current techniques used to forecast time-boxed financial data, such as the value of a stock on the stock market, before detailing the design and implementation of the model. Having completed the model, the performance of selected techniques is reviewed, before discussing further research opportunities which could improve the model’s accuracy.