Fraud Detection Using Data Mining

Abstract

Data mining combines data analysis techniques with high-end technology for use within a process. The primary goal of data mining is to develop usable knowledge regarding future events. This paper defines the steps in the data mining process, explains the importance of the steps, and shows how the steps were used in two case studies involving fraud detection. The steps in the data mining process are: • Problem definition • Data collection and enhancement • Modeling strategies • Training, validation, and testing of models • Analyzing results • Modeling iterations • Implementing results. In this case study, a public sector organization deploys data mining in a purchase card domain with the aim of determining what transactions reflect fraudulent transactions in the form of diverting public funds for private use. In this study, called “the purchase card case,” knowledge of fraud does not exist.