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 Intelligent Data Mining System to Detect Health Care Fraud

The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice

Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

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-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

Data mining for detecting Bitcoin Ponzi schemes

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives

Losing the War Against Dirty Money: Rethinking Global Standards on Preventing Money Laundering and Terrorism Financing

Following a brief overview in Part I.A of the overall system to prevent money laundering, Part I.B describes the role of the private sector, which is to identify customers, create a profile of their legitimate activities, keep detailed records of clients and their transactions, monitor their transactions to see if they conform to their profile, examine further any unusual transactions, and report to the government any suspicious transactions. Part I.C continues the description of the preventive measures system by describing the government\u27s role, which is to assist the private sector in identifying suspicious transactions, ensure compliance with the preventive measures requirements, and analyze suspicious transaction reports to determine those that should be investigated. Parts I.D and I.E examine the effectiveness of this system. Part I.D discusses successes and failures in the private sector\u27s role. Borrowing from theory concerning the effectiveness of private sector unfunded mandates, this Part reviews why many aspects of the system are failing, focusing on the subjectivity of the mandate, the disincentives to comply, and the lack of comprehensive data on client identification and transactions. It notes that the system includes an inherent contradiction: the public sector is tasked with informing the private sector how best to detect launderers and terrorists, but to do so could act as a road map on how to avoid detection should such information fall into the wrong hands. Part I.D discusses how financial institutions do not and cannot use scientifically tested statistical means to determine if a particular client or set of transactions is more likely than others to indicate criminal activity. Part I.D then turns to a discussion of a few issues regarding the impact the system has but that are not related to effectiveness, followed by a summary and analysis of how flaws might be addressed. Part I.E continues by discussing the successes and failures in the public sector\u27s role. It reviews why the system is failing, focusing on the lack of assistance to the private sector in and the lack of necessary data on client identification and transactions. It also discusses how financial intelligence units, like financial institutions, do not and cannot use scientifically tested statistical means to determine probabilities of criminal activity. Part I concludes with a summary and analysis tying both private and public roles together. Part II then turns to a review of certain current techniques for selecting income tax returns for audit. After an overview of the system, Part II first discusses the limited role of the private sector in providing tax administrators with information, comparing this to the far greater role the private sector plays in implementing preventive measures. Next, this Part turns to consider how tax administrators, particularly the U.S. Internal Revenue Service, select taxpayers for audit, comparing this to the role of both the private and public sectors in implementing preventive measures. It focuses on how some tax administrations use scientifically tested statistical means to determine probabilities of tax evasion. Part II then suggests how flaws in both private and public roles of implementing money laundering and terrorism financing preventive measures might be theoretically addressed by borrowing from the experience of tax administration. Part II concludes with a short summary and analysis that relates these conclusions to the preventive measures system. Referring to the analyses in Parts I and II, Part III suggests changes to the current preventive measures standard. It suggests that financial intelligence units should be uniquely tasked with analyzing and selecting clients and transactions for further investigation for money laundering and terrorism financing. The private sector\u27s role should be restricted to identifying customers, creating an initial profile of their legitimate activities, and reporting such information and all client transactions to financial intelligence units

Critical success factors for preventing E-banking fraud

E-Banking fraud is an issue being experienced globally and is continuing to prove costly to both banks and customers. Frauds in e-banking services occur as a result of various compromises in security ranging from weak authentication systems to insufficient internal controls. Lack of research in this area is problematic for practitioners so there is need to conduct research to help improve security and prevent stakeholders from losing confidence in the system. The purpose of this paper is to understand factors that could be critical in strengthening fraud prevention systems in electronic banking. The paper reviews relevant literatures to help identify potential critical success factors of frauds prevention in e-banking. Our findings show that beyond technology, there are other factors that need to be considered such as internal controls, customer education and staff education etc. These findings will help assist banks and regulators with information on specific areas that should be addressed to build on their existing fraud prevention systems