Title: Benford's law
Description: "A powerful new tool for all forensic accountants, or anyone who analyzes data that may have been altered. Benford's Law gives the expected patterns of the digits in the numbers in tabulated data such as town and city populations or Madoff's fictitious portfolio returns. Those digits, in unaltered data, will not occur in equal proportions; there is a large bias towards the lower digits, so much so that nearly one-half of all numbers are expected to start with the digits 1 or 2.  These patterns were originally discovered by physicist Frank Benford in the early 1930s, and have since been found to apply to all tabulated data. Mark J. Nigrini has been a pioneer in applying Benford's Law to auditing and forensic accounting, even before his groundbreaking 1999 Journal of Accountancy article introducing this useful tool to the accounting world.  In Benford's Law, Nigrini shows the widespread applicability of Benford's Law and its practical uses to detect fraud, errors, and other anomalies.  Explores primary, associated, and advanced tests, all described with data sets that include corporate payments data and election data  Includes ten fraud detection studies, including vendor fraud, payroll fraud, due diligence when purchasing a business, and tax evasion  Covers financial statement fraud, with data from Enron, AIG, and companies that were the target of hedge fund short sales  Looks at how to detect Ponzi schemes, including data on Madoff, Waxenberg, and more  Examines many other applications, from the Clinton tax returns and the charitable gifts of Lehman Brothers to tax evasion and number invention  Benford's Law has 250 figures and uses 50 interesting authentic and fraudulent real-world data sets to explain both theory and practice, and  concludes with an agenda and directions for future research. The companion website adds additional information and resources"--
Subjects: Fraud, Forensic accounting, BUSINESS & ECONOMICS / Accounting / General, Accounting fraud, Prevention, Fraud, prevention