Click fraud–the deliberate clicking on advertisements with no real interest on the product or service offered–is one of the most daunting problems in online advertising. Building an effective fraud detection method is thus pivotal for online advertising businesses. We organized a Fraud Detection in Mobile Advertising (FDMA) 2012 Competition, opening the opportunity for participants to work on r...

Our work focuses on: the problem of detecting unusual changes of consumption in mobile phone users, the corresponding building of data structures which represent the recent and historic users’ behaviour bearing in mind the information included in a call, and the complexity of the construction of a function with so many variables where the parameterization is not always known. 1 Description of t...

In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which ...

As technology advances, fraud is becoming increasingly complicated and difficult to detect, especially when individuals collude. Surveys show that the median loss from collusive fraud is much greater than fraud perpetrated by individuals. Despite its prevalence and potentially devastating effects, internal auditors often fail to consider collusion in their fraud assessment and detection efforts...

A hybrid influence diagram is a compact graphical and numerical representation of a decision problem under uncertainty that includes both discrete and continuous chance variables. These models can be used by businesses to detect online credit card transactions that may be fraudulent. By creating decision rules based on merchandise value and additional address and product characteristics, the in...

-Neural computing refers to a pattern recognition methodology for machine learning. The resulting model from neural computing is often called an artif icial neural network (ANN) or a neural network. Neural networks have been used in many business applications for pattern recognition, forecasting, prediction and classif ication. Neural network computing is a key component for any data mining too...

Research background: The issue of fraud is a real and not an exceptional phenomenon in today’s global economies. Fraud arises businesses at different levels from motivations. However, with the development fraud, methods are also being developed to help detect such fraud. Therefore, present paper focused on creative accounting as one tools for detecting these scams. consists four parts. first pa...

In this study we develop a method which improves a credit card fraud detection solution currently being used in a bank. With this solution each transaction is scored and based on these scores the transactions are classified as fraudulent or legitimate. In fraud detection solutions the typical objective is to minimize the wrongly classified number of transactions. However, in reality, wrong clas...

In the telecommunications services, fraud situations have a significant business impact. Due to the massive amounts of data handled, fraud detection stands as a very difficult and challenging task. In this paper, we propose the application of dynamic clustering over signatures to support this task. Traditional static clustering is applied to determine clusters characteristics, and dynamic clust...

This appendix reports the results for the baseline and yearly/quarterly context-based classifiers when using the 1:10 regulator cost setting. Since the AUC values are computed across different cost settings (and are therefore the same for the investor and regulator situations), we report only the legitimate/fraud recall rates. Overall AUC values as well as results for the investor cost setting ...