Applying game theory rules to enhance decision support systems in credit and financial applications

This paper 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 use the German credit dataset using a variety of different data mining mechanisms then we propose a combined model to enhance the results using Game Theory principles and the decision tree “J48” algorithm as a data mining mechanism.