Credit card fraud detection by adaptive neural data mining

The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate

Intelligent Financial Fraud Detection Practices: An Investigation

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.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014

The role of IT/IS in combating fraud in the payment card industry

The vast growth of the payment card industry (PCI) in the last 50 years has placed the industry in the centre of attention, not only because of this growth, but also because of the increase of fraudulent transactions. The conducted research in this domain has produced statistical reports on detection of fraud, and ways of protection. On the other hand, the relevant body of research is quite partial and covers only specific topics. For instance, the provided reports related to losses due to fraudulent usage of cards usually do not present the measures taken to combat fraud nor do they explain the way fraud happens. This can turn out to be confusing and makes one believe that card usage can be more negative than positive. This paper is intended to provide accumulative and organized information of the efforts made to protect businesses from fraud. We try to reveal the effectiveness and efficiency of the current fraud combating techniques and show that organized worldwide efforts are needed to take care of the larger part of the problem. The research questions that will be addressed in the paper are: 1) how can IT/IS help in combating fraud in the PCI?, and 2) is the implemented IT/IS effective and efficient enough to bring progress in combating fraud? Our research methodology is based on a case study conducted in a Macedonian bank. The research is explorative and will be mostly qualitative in nature; however some quantitative aspects will be included. The findings indicate that fraud can take up many forms. A classification of the different forms of data theft into different fraudulent appearances was made. We showed that the benefits from implementing the fraud reduction efforts are multiple. Results show that a bank has to be very small to experience losses from fixed expenditures coming from the implementation of the fraud reduction IT/IS. Medium-sized and large banks should not even see any problems arising from those expenditures. Based on the empirical data and the presented facts we can conclude that the fraud reduction IT/IS do have a positive effect on all sides of the payment process and fulfills the expectations of all stakeholders

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists

Payments fraud : consumer considerations

This article examines the potential for fraud associated with various "traditional" payment methods and the protective measures that consumers should take when using them.Payment systems ; Checks ; Credit cards