Data Mining for Fraud Detection: An Overview of Techniques and Applications

Abstract

The process of data mining involves extracting knowledge and insights from vast amounts of data. It can be done through the use of various computational and statistical techniques to identify anomalous, correlational, and pattern patterns. On the other hand, fraud detection is a process that involves identifying and preventing activities that are fraudulent. This process can be carried out through the use of various technologies and techniques, such as artificial intelligence and data mining. It aims to minimize the financial losses caused by these types of activities and ensure that the company follows proper regulatory and legal requirements. The ability to identify fraud prior to it happening is very important for organizations to prevent it from happening. This paper looks into the various aspects of data mining and how it can be utilized for fraud detection. This paper discusses the various aspects of fraud detection and how it can be utilized for organizations to prevent it from happening. We then talk about the various techniques that are used for this process, such as clustering, unsupervised learning, and neural networks. In addition, we talk about the various data preprocessing techniques that are used in the detection of fraud. These include data normalization, feature selection, and extraction. Data visualization is also important in interpreting and understanding the results of mining analyses. The paper then covers the various fraud detection applications of data mining. These include healthcare fraud, credit card fraud, financial statement fraud, and insurance fraud. We provide examples of how these techniques have been utilized to identify fraudulent activities. The paper then discusses the limitations of mining data for fraud detection, as well as the need for an integrated approach that combines various techniques, such as human intervention and audit trails. This paper provides an extensive overview of the various aspects of this field and highlights the significance of this technology in the fight against financial crime.