Fraud In Enron's Failure

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Starting in 1990s, a wave of corporate frauds in the United States occurred with Enron’s failure perhaps being the emblematic example. Jeffords (1992) examined 910 cases of frauds submitted to the “Internal Auditor” during the nine-year period from 1981 to 1989 to assess the specific risk factors cited in the Treadway Commission Report. He concluded that “approximately 63 percent of the 910 fraud cases are classified under the internal control risks.”

Calderon and Green (1994) did an analysis of 114 actual cases of business fraud published in the “Internal Auditor” during 1986 to 1990. They concluded that limited separation of duties, false documentation, and inadequate (or non-existent) control were the main reasons for 60% of the fraud
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Major cause for perpetration of fraud is laxity in observance in laid-down system and procedures by supervising staff. Harris and William (2004), however, examined the reasons for ‘loan’ frauds in banks and highlighted on due persistent program. They concluded that lack of an effective internal audit staff in the company, frequent changes of management and directors, appointment of unqualified staffs in important audit or finance posts, customer’s reluctance to provide requested information or financial statements and false data provided by the customers are the main reasons for loan frauds.
Beirstaker et al. (2005) in their study provided numerous fraud protection and detection techniques. Rezaee (2005) however, finds five factors that explain the several high-profile ‘financial statement’ frauds. These factors are: cooks, recipes, incentives, monitoring and end results. Willison (2006) examined the reasons that are responsible for the breakdown of ‘Barring’ Bank. The collapse resulted due to the failures of management, financial and operational controls of Baring
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Through the use of computer simulation models, it shows how a combination of CEO’s selfishness, financial incentive, shareholders’ expectations and subordinate silence as well as CEO’s dishonesty can do much to explain some of the findings highlighted in recent high-profile financial accounting scandals.” Cecchini et al. (2010) provided a methodology for detecting ‘management’ fraud using basic financial data based on ‘support vector machines’. A large experimental data set was collected, which included quantitative financial attributes for fraudulent and non-fraudulent public companies. They concluded that “Support vector machines using the financial kernel correctly labelled 80% of the fraudulent cases and 90.6% of the non-fraudulent cases on a holdout set. The results also show that the methodology has predictive value because, using only historical data, it was able to distinguish fraudulent from non-fraudulent companies in subsequent

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