Medical insurance frauds are causing huge losses for public healthcare funds in many countries. Detecting medical insurance frauds is an important and difficult challenge. Because of the complex granularity of data, existing fraud detection approaches tend to be less effective in terms of recalling fraudulent claim behaviours. In this paper, we propose a Hybrid Fraud Detection Approach (HFDA) t...

Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing detection systems, which are able to automatically detect and block fraudulent transactions. From machine learning perspective, the task of detecting transactions binary classification problem. Classification models commonly trained evalu...

Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. Fraud is one of the major ethical issues in the credit card industry. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In rea...

Insurance fraud costs the property and casualty insurance industry over 25 billion dollars (USD) annually. This paper addresses workers' compensation claim fraud. A data mining approach is adopted, and issues of data preparation are discussed. The focus is on building predictive models to score an open claim for a propensity to be fraudulent. A key component to modeling is the use of textual da...

Internet auction fraud has become prevalent. Methodologies for detecting fraudulent transactions use historical information about Internet auction participants to decide whether or not a user is a potential fraudster. The information includes reputation scores, values of items, time frames of various activities and transaction records. This paper presents a distinctive set of fraudster characte...

Smartphone advertisement is increasingly used among many applications and allows developers to obtain revenue through in-app advertising. Our study aims at identifying potential security risks of mobile-based advertising services where advertisers are charged for their advertisements on mobile applications. In the Android platform, we particularly implement bot programs that can massively gener...

Reputation fraud campaigns (RFCs) distort the reputations of rated items, by generating fake ratings through multiple spammers. One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories. However, these campaigns are constantly evolving and changing tactics to evade detection. For example, they can launch early attacks on the items to quickly dom...

A lot of transactions occur in banking sector due to day to day operations. E-Commerce is widely used in busy life. In E-commerce life, credit card transactions are increasing day by day. This will increase in frauds in credit card. Credit card fraud is a major problem in financial industry. Many technologies have been developed to reduce the fraud in credit card such as data mining fuzzy logic...

The problem of detecting scientific fraud using machine learning was recently introduced, with initial, positive results from a model taking into account various general indicators. The results seem to suggest that writing style is predictive of scientific fraud. We revisit these initial experiments, and show that the leave-one-out testing procedure they used likely leads to a slight over-estim...