Using data from a credit card issuer, a neural network based fraud detection system was trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity over a subsequent two-month period of time. The neural network was trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud,...

Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-...

Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way of reducing fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are esse...

With the rise and swift growth of E-Commerce, credit card uses for online purchases has increased dramatically and it caused sudden outbreak in the credit card fraud. Fraud is one of the major ethical issues in the credit card industry. With both online as well as regular purchase, credit card becomes the most popular mode of payment with cases of fraud associated with it are also increasing. A...

The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificia...

Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have be...

Fraud detection in healthcare is an important yet difficult problem. We present a fraud screening solution to identify suspicious pharmacies from a large dataset of pharmacy claims. Our solution has stemmed from collaboration with medical claim investigators and proven usefulness to investigators by discovering real fraud cases. We focus on a concrete problem of probabilistic outlier detection ...

It is estimated that between $600 and $850 billion annually is lost to fraud, waste, and abuse in the US healthcare system, with $125 to $175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matched government program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularly susceptible targets for fraud in recen...

Fraud detection and prevention systems are based on various technological paradigms but the two prevailing approaches are rule-based reasoning and data mining. In this paper we claim that ontologies, an increasingly popular and widely accepted knowledge representation paradigm, can help both of these approaches be more efficient as far as fraud detection is concerned and we introduce a methodol...

In this study, I investigate the economic determinants of firms’ propensity to commit securities fraud and the determinants of fraud detection. The analysis is based on a new handcompiled fraud sample of private securities class action litigation suits filed between 1996 and 2003 involving allegations of accounting irregularities. I use econometric methods to account for the unobservability of ...