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Financial Statement Fraud (financial + statement_fraud)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	100%

Selected Abstracts

A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE & MANAGEMENT, Issue 1-2 2007
Bethany Hoogs
This study presents a genetic algorithm approach to detecting financial statement fraud. The study uses a sample comprising a target class of 51 companies accused by the Securities and Exchange Commission of improperly recognizing revenue and a peer class of 339 companies matched on industry and size (revenue). Variables include 76 comparative metrics, based on specific financial metrics and ratios that capture company performance in the context of historical and industry performance, and nine company characteristics. Time-based patterns detected by the genetic algorithm accurately classify 63% of the target class companies and 95% of the peer class companies. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Using Nonfinancial Measures to Assess Fraud Risk

JOURNAL OF ACCOUNTING RESEARCH, Issue 5 2009
JOSEPH F. BRAZEL
ABSTRACT This study examines whether auditors can effectively use nonfinancial measures (NFMs) to assess the reasonableness of financial performance and, thereby, help detect financial statement fraud (hereafter, fraud). If auditors or other interested parties (e.g., directors, lenders, investors, or regulators) can identify NFMs (e.g., facilities growth) that are correlated with financial measures (e.g., revenue growth), inconsistent patterns between the NFMs and financial measures can be used to detect firms with high fraud risk. We find that the,difference,between financial and nonfinancial performance is significantly greater for firms that committed fraud than for their nonfraud competitors. We also find that this difference is a significant fraud indicator when included in a model containing variables that have previously been linked to the likelihood of fraud. Overall, our results provide empirical evidence suggesting that NFMs can be effectively used to assess fraud risk. [source]

Digital analysis: A better way to detect fraud

JOURNAL OF CORPORATE ACCOUNTING & FINANCE, Issue 4 2007
James A. Tackett
The Sarbanes-Oxley Act (SOX) has put corporate executives under the gun when it comes to detecting financial statement fraud. Unfortunately, most methods for discovering fraud are expensive and time-consuming. But there is one fast, inexpensive method you may not be using: digital analysis. The author explains what it is and how to use it. © 2007 Wiley Periodicals, Inc. [source]