***** Data Mining for Intelligence, Fraud, and Criminal Detection. By Christopher Westphal; published by CRC Press, www.crcpress.com (Web); 440 pages; $69.95.
The past 15 years have seen the development of enormous databases by government and the private sector. With the increase in the amount of data, the profession of data analysis has evolved, and the job market is rapidly growing, with more and more colleges offering degrees in this intelligence field. One increasingly common practice is that of data mining—identifying hidden patterns in large amounts of data.
With Data Mining for Intelligence, Fraud, and Criminal Detection, Christopher Westphal has written an extremely useful, interesting, and advanced book on data mining and its applications.
The text first defines data patterns in general, and addresses data quality and validity. The section on “real-world examples and operations” is a most interesting one, concentrating on intelligence and crimes including money laundering. Westphal offers an interesting discussion of money services businesses (MSBs). An MSB is a business other than a bank that provides services such as check cashing, money order processing, and wire transmissions. These operations provide fertile ground for fraud and terrorist financing.
Also featured is a detailed discussion of U.S. and international procedures for detecting and fighting money laundering. For example, in the United States, financial institutions file suspicious activity reports (SARs) with the Financial Crimes Enforcement Network (FinCEN). According to Westphal, 15 million SARs are filed annually by banks, casinos, insurance companies, and other businesses, and that number is increasing each year. Banks, meanwhile, are required to identify and extract suspicious data from millions of transactions through in-house financial intelligence units (FIUs). Using automatic data mining programs, the financial institutions can analyze the transactions to identify which merit SARs.
Finally, the book takes on the new and dynamic phenomenon of law enforcement information sharing and intelligence fusion centers. Westphal acknowledges that this was the most difficult section of the book to write. New systems of sharing are being introduced constantly, and he encourages readers to research these new systems independently.
The book is well organized and includes a useful table of contents and index. Additionally, the author provides numerous case examples and illustrations and charts.
Westphal has written an outstanding book that is highly recommended for the advanced security practitioner with a stake in data analysis. Further, 100 percent of the profits from the book are being donated to the National Law Enforcement Officers Memorial Fund, which is highly commendable.
Reviewer: Adrian A. Barnie, CPP, CFE (Certified Fraud Examiner), CAMS (Certified Anti-Money Laundering Specialist), is with the Anti-Money Laundering Unit of KeyBank’s Financial Intelligence Unit in Cleveland, Ohio. He is a member of ASIS International.