ENHANCING RISK PREDICTION IN FINANCIAL APPLICATIONS USING DATA MINING AND GAME THEORY PRINCIPLES

Abstract

This thesis examines the potential of applying Game Theory to Data Mining mechanisms to enhance the accuracy of predicting risk in .financial settings. There have been many attempts made in the past to enhance Data Mining results using different methods including Game Theory principles. Despite the promising results of previous work in integrating Game Theory and Data Mining, further research is needed to explore the potential of creating a combined model that can be applied to a range of datasets to successfully enhance risk prediction. We apply a variety of different tree data mining algorithms to the German Credit Dataset. Then, we propose a combined model to enhance the accuracy of the data mining results by using Game Theory principles. Our approach focuses on correcting the error from the incorrectly classified instances by our proposed enhanced game tree model. By using the payoff table derived from our enhanced game tree model and the binomial distribution, we can determine the percentage of enhancement to the tree-based data mining results. Our results show that applying Game Theory principles to Data Mining techniques in a combined model can improve overall accuracy and enhance decision support systems in financial applications.