Research Article
Intelligent Financial Fraud Detection Practices: An Investigation
@INPROCEEDINGS{10.1007/978-3-319-23802-9_16, author={Jarrod West and Maumita Bhattacharya and Rafiqul Islam}, title={Intelligent Financial Fraud Detection Practices: An Investigation}, proceedings={International Conference on Security and Privacy in Communication Networks. 10th International ICST Conference, SecureComm 2014, Beijing, China, September 24-26, 2014, Revised Selected Papers, Part II}, proceedings_a={SECURECOMM}, year={2015}, month={12}, keywords={Financial fraud Computational intelligence Fraud detection techniques Data mining}, doi={10.1007/978-3-319-23802-9_16} }- Jarrod West
Maumita Bhattacharya
Rafiqul Islam
Year: 2015
Intelligent Financial Fraud Detection Practices: An Investigation
SECURECOMM
Springer
DOI: 10.1007/978-3-319-23802-9_16
Abstract
Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.
