Detecting Frauds and Money Laundering: A Tutorial | SpringerLinkThe purpose of this tutorial is to provide an introduction to the general area of frauds to analytics scientists and professionals and discuss some analytics techniques used in their detection. We focus on frauds in insurance, stock markets and on money laundering. There are survey papers , ,  and books , , , ,  that discuss various analytics techniques for fraud detection in general. However, they do not survey analytics for stock market frauds and money laundering. Another important contribution is that we also discuss some open areas and research problems in the field. Unable to display preview.
Data analysis techniques for fraud detection
Fraud is a billion-dollar business and it is increasing every year. The PwC global economic crime survey of  found that half 49 percent of the 7, companies they surveyed had experienced fraud of some kind. Fraud possibilities co-evolve with technology, esp. Information technology  Business reengineering, reorganization or downsizing may weaken or eliminate control, while new information systems may present additional opportunities to commit fraud. Traditional methods of data analysis have long been used to detect fraud. They require complex and time-consuming investigations that deal with different domains of knowledge like financial, economics, business practices and law.
You are currently using the site but have requested a page in the site. Would you like to change to the site? Delena D. Fraud Analytics thoroughly reveals the elements of analysis that are used in today's fraud examinations, fraud investigations, and financial crime investigations. This valuable resource reviews the types of analysis that should be considered prior to beginning an investigation and explains how to optimally use data mining techniques to detect fraud. Packed with examples and sample cases illustrating pertinent concepts in practice, this book also explores the two major data analytics providers: ACL and IDEA.